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No exact match for this specialty yet — here are related experts from our network.

Aditya P.
Available Now
Verified in SoftDoesAditya P.
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Aditya P.
Available Now
Aditya P.Verified in SoftDoes
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Aditya P.
Available Now
Aditya P.Verified in SoftDoes
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Andrea M.
Available Now
Verified in SoftDoesAndrea M.
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrea M.
Available Now
Andrea M.Verified in SoftDoes
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrea M.
Available Now
Andrea M.Verified in SoftDoes
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrii V.
Available Now
Verified in SoftDoesAndrii V.
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Andrii V.
Available Now
Andrii V.Verified in SoftDoes
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Andrii V.
Available Now
Andrii V.Verified in SoftDoes
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Boris S.
Available Now
Verified in SoftDoesBoris S.
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Boris S.
Available Now
Boris S.Verified in SoftDoes
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Boris S.
Available Now
Boris S.Verified in SoftDoes
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Eugene M.
Available Now
Verified in SoftDoesEugene M.
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Eugene M.
Available Now
Eugene M.Verified in SoftDoes
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Eugene M.
Available Now
Eugene M.Verified in SoftDoes
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Hripsime S.
Available Now
Verified in SoftDoesHripsime S.
AI Engineer | Data Scientist
FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Hripsime S.
Available Now
Hripsime S.Verified in SoftDoes
AI Engineer | Data Scientist
FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Hripsime S.
Available Now
Hripsime S.Verified in SoftDoes
AI Engineer | Data Scientist
FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Mario J.
Available Now
Verified in SoftDoesMario J.
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Mario J.
Available Now
Mario J.Verified in SoftDoes
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Mario J.
Available Now
Mario J.Verified in SoftDoes
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Raphael O.
Available Now
Verified in SoftDoesRaphael O.
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Raphael O.
Available Now
Raphael O.Verified in SoftDoes
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Raphael O.
Available Now
Raphael O.Verified in SoftDoes
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Santiago G.
Available Now
Verified in SoftDoesSantiago G.
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Santiago G.
Available Now
Santiago G.Verified in SoftDoes
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Santiago G.
Available Now
Santiago G.Verified in SoftDoes
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Thierry M.
Available Now
Verified in SoftDoesThierry M.
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thierry M.
Available Now
Thierry M.Verified in SoftDoes
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thierry M.
Available Now
Thierry M.Verified in SoftDoes
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thomas S.
Available Now
Verified in SoftDoesThomas S.
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Thomas S.
Available Now
Thomas S.Verified in SoftDoes
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Thomas S.
Available Now
Thomas S.Verified in SoftDoes
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Tzechung K.
Available Now
Verified in SoftDoesTzechung K.
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

Tzechung K.
Available Now
Tzechung K.Verified in SoftDoes
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

Tzechung K.
Available Now
Tzechung K.Verified in SoftDoes
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

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What our Embedded Software Developers can build

Not sure which engagement model fits?

SoftDoes takes full ownership of delivery, combining project management, engineering, design, and QA into one accountable team focused on successful outcomes.

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RIGHT expert, FASTER

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How to hire a Embedded Software Developer

01
BROWSE PROFILESRIGHT NOW

Fill out a short form and see who's on the bench. Real profiles, verified histories.

02
Interview1-3 DAYS

Tell us what you need. We propose two or three candidates from the bench; you interview them directly.

03
OnboardWEEK ONE

Your engineer starts on your project. Contract, payments, and the guarantee run through us.

US VS. THE DATABASE

Time to Start
Talent Quality
Technical Vetting
Flexibility
Operational Overhead
Cost Efficiency
cursor
<SoftDoes>
Time to Start
1-2 weeks
Talent Quality
Senior-only engineers
Technical Vetting
Multi-stage screening
Flexibility
Scale up or down anytime
Operational Overhead
As managed as you want
Cost Efficiency
Competitive, fee-free
Talent Marketplaces
Time to Start
1-3 months
Talent Quality
Mixed experience levels
Technical Vetting
One screen, then gone
Flexibility
Contract restrictions
Operational Overhead
Partially managed
Cost Efficiency
Agency markup
In-House Hiring
Time to Start
2-6 months
Talent Quality
Depends on market
Technical Vetting
Internal responsibility
Flexibility
Long-term commitment
Operational Overhead
Fully internal
Cost Efficiency
Highest total cost

Frequently Asked Questions

Everything you need to know about deploying, scaling, and securing your neural agents with SoftDoes. Can’t find an answer?

How long does it take to hire an Embedded Software Developer through SoftDoes?

When you work with SoftDoes, sourcing a shortlist of vetted embedded software developers typically takes three to six weeks because we maintain an active network of prescreened engineers with proven production experience. For especially rare skill sets or niche regulatory domains such as automotive safety or medical device compliance, that timeline may extend to six to ten weeks. After placement, expect another two to three months of ramp up before the engineer reaches full feature ownership, depending on your codebase complexity, hardware access, and test environment. From initial engagement to full value delivery, plan for a total of roughly five to nine months in the most demanding product areas, though our structured onboarding protocol is designed to compress that timeline wherever possible.

What does it cost to hire an Embedded Software Developer?

Total cost of hiring an embedded software engineer goes well beyond base salary. Embedded software developers earn between $120,000 and $145,000 annually in North America as a baseline, but the true cost of employment typically runs 30% to 40% above that figure once you account for benefits, payroll taxes, recruiting fees, tooling, hardware kits, and onboarding time. Outsourcing or using a staff augmentation partner with nearshore or Eastern European talent can reduce the effective cost by 50% to 60% for comparable delivery quality, though you must carefully manage domain alignment and IP control. SoftDoes helps you find the right balance between cost efficiency and engineering quality so your budget delivers maximum impact.

What engagement models are available (dedicated hire, pod, contract)?

SoftDoes offers multiple engagement models tailored to your scaling needs. A dedicated hire or remote embedded engineer works as an extension of your team through our talent network, ideal when you need speed, lower fixed overhead, and the flexibility to scale. A pod model gives you a small, cohesive embedded team with dedicated resources that integrates with your hardware and product groups, best when your roadmap spans multiple firmware subsystems. For defined scope work such as an OTA framework or a driver stack, a contract or project based engagement delivers specific deliverables on a fixed timeline. Finally, full time in house placement is available when firmware and IP are core to your product and long term retention of domain knowledge is paramount. Each model is designed around your strategic risk tolerance, control requirements, and budget.

How do you ensure time zone alignment with an Embedded Software Developer?

Time zone alignment is built into our matching criteria from the start. SoftDoes leverages North America focused partnerships and nearshore networks to ensure meaningful overlap windows with US and Canadian engineering teams. We establish dedicated overlapping hours, core meeting times, and clear expectations around synchronous versus asynchronous work. For engagements with partial overlap, we compensate with structured documentation practices, joint design reviews, and shared observability tools so that no critical context is lost between sessions. When onboarding a remote embedded engineer, we plan cross team visibility and communication rituals to ensure seamless collaboration regardless of geography.

How does SoftDoes technically vet an Embedded Software Developer?

Our vetting process is multi stage and engineered to surface real capability, not resume polish. First, we verify the candidate's past shipping record by requesting specific examples of hardware bring up, production embedded firmware, field reliability outcomes, and modules or systems they owned. Second, we conduct a deep technical interview using scenario based architecture questions, performance and power optimization challenges, and debugging exercises with limited observability. Third, candidates complete a hands on coding assessment, typically a C or C++ driver or protocol implementation test, a unit test or kernel module exercise, and a field failure diagnostic case. For regulated industries, we evaluate domain safety and security relevance including certifications, compliance practice, and experience with applicable safety standards. Finally, we assess communication and collaboration by evaluating whether the candidate can translate technical tradeoffs to product, safety, hardware, and operations stakeholders and by looking for evidence of leadership and mentoring even without a formal title.

What happens if the Embedded Software Developer isn't the right fit, or I need to scale up or down?

SoftDoes provides a zero risk replacement guarantee. If a hire fails to meet agreed upon standards for skills, ramp milestones, or deliverables within a specified period, we replace them at no additional cost to you. We also build scalability clauses into every engagement so you can scale your embedded team up or down as product demands shift without excessive switching costs. We document deliverables and success criteria early in onboarding so that "fit" is objectively measurable rather than subjective. If performance issues surface early, we activate remediation or replacement plans immediately to protect your project timeline and budget from sunk cost drag.

The Executive Guide to Hiring an Embedded Software Developer

A single misfire on an embedded software hire can stall a product launch by months and burn through six figures in wasted salary, ramp time, and opportunity cost. Multiply that by the razor thin supply of senior embedded engineers who can actually ship production firmware, and you have a hiring problem that belongs on the executive agenda. This playbook gives you a field tested strategy to define, vet, and onboard top tier embedded software developer talent, built from real world lessons in the engineering trenches so you can scale your team without bleeding time or budget.

What Actually Separates a Senior Embedded Software Developer from Everyone Else

The Operational Realities That Define True Expertise

Most job descriptions for an embedded software engineer read like a copy paste of technical keywords. That tells you nothing about whether a candidate can own outcomes in a resource constrained, hardware coupled environment. Here is what a senior embedded firmware engineer actually does every day, and why it matters to your bottom line:

  • Owns firmware subsystems end to end. That means the bootloader, memory and power management, OTA update pipeline, driver architecture, and BSP layers. They define module boundaries, not just fill in function bodies. Successful board bring up demonstrates an understanding of hardware software integration that separates veterans from juniors.
  • Manages brutal resource constraints. CPU cycles, RAM and flash budgets, power draw across sleep and wake cycles, thermal behavior. They make tradeoff decisions under constraints that application developers never face, working directly with microcontrollers, sensors, and peripherals where every byte of memory counts.
  • Commands real time operating systems and hardware interfaces. RTOS scheduling, interrupt service routines versus thread context, latency determinism, priority inversions, plus protocols like SPI, I2C, UART, CAN, and USB. Candidates must understand task scheduling and interrupt service routines in an RTOS environment to be effective. Embedded developers often work with communication protocols like I2C and SPI as core daily tools.
  • Makes critical architecture decisions. When to go bare metal, when to use an RTOS like FreeRTOS or Zephyr, when embedded Linux is the right call. When to prioritize safety compliance over speed, or performance over maintainability versus cost. This is the kind of judgment that only comes from years of professional experience shipping real devices.
  • Builds reliability and observability into firmware from day one. Structured logging, crash dump post mortems, HIL (hardware in loop) testing, fault injection, rollback safety, and fleet diagnostics for connected smart devices and IoT products. Bugs in embedded systems can be difficult and expensive to fix post deployment, making proactive reliability engineering a non negotiable skill.
  • Leads without a title. Mentoring mid level engineers, setting code review standards, documenting architecture decisions, and collaborating with cross functional teams including hardware designers, product managers, safety, and operations. A senior embedded engineer should be able to manage architecture decisions and mentor others.

This is the difference between someone who sets durable technical direction and someone who simply implements feature specs handed to them.

The Financial and Strategic Case for Getting This Hire Right

Hiring the right embedded specialist delivers measurable ROI. Hiring wrong, or too slowly, compounds cost in ways most executives underestimate:

  • Technical debt reduction. Fixing poor driver and hardware integration, replacing brittle abstractions, and updating build toolchains reduces your maintenance backlog, lowers bug counts, and shrinks long term cost of ownership. Strong fundamentals in memory management and debugging are essential for embedded roles precisely because they prevent this debt from accumulating.
  • Faster time to market. A deeply specialized embedded software hire often takes three to six months to source, plus another two to three months to ramp before delivering value. Every month of delay in product shipping burns tens of thousands in direct costs and opportunity cost. Speed here is a competitive weapon.
  • Risk mitigation at the product level. Field failures, safety noncompliance, firmware security vulnerabilities, recalls, regulatory penalties. A senior hire who builds reliability into firmware from the start reduces RMA rates, liability exposure, and brand damage. Knowledge of secure boot and hardware based cryptography may be necessary for compliance in regulated industries.
  • Infrastructure and operational efficiency. Better OTA pipelines, CI/CD for firmware, observability, and automated builds reduce downtime, avoid rework, shrink defect escape rates, and improve patch response windows. These are not "nice to haves" for a production embedded platform; they are baseline expectations for any senior embedded software engineer.

The global embedded systems market will grow from $178 billion to $284 billion by the next decade. Demand for embedded engineers is strong in automotive and healthcare industries. The companies that build hiring muscle now will capture disproportionate market share in robotics, medical devices, automotive, and industrial automation.

How to Prepare Before You Start Searching

Audit Your Technical Constraints First

Before you write a single job spec or talk to a single candidate, you need to know exactly what problem this hire must solve. Skipping this step is how companies end up with a technically competent engineer who is solving the wrong problem.

Identify Your Architecture and Debt Exposure

Ask your engineering leads: what are the current technical barriers? Is the codebase scaling? Are builds reliable? Is hardware integration fragile? Is there missing test coverage, energy or power issues, or security vulnerability exposure? Define the most pressing technical debt: unsustainable driver code, undefined failure modes, lack of automated tests or HIL, outdated chipsets or toolchains. The hire's mission starts here. If you cannot articulate the top three firmware problems costing you time and money, you are not ready to interview.

Determine Team Dynamics and Autonomy Level

Decide whether the incoming embedded engineer will work deep in a hardware and software product pod as an embedded specialist who owns cross functional outcomes, or simply be an implementer in a larger orchestration receiving guidance from a principal engineer. The autonomy level matters enormously in both vetting and compensation. A senior candidate who thrives with architectural freedom will disengage fast if micromanaged. A mid level engineer given too much autonomy too soon will create risk.

Choose the Right Deployment Model

Your engagement model is a strategic decision, not an HR checkbox:

  • In house FTE gives you the tightest IP control and long term coherence but carries the highest cost and longest recruiting timeline.
  • Dedicated remote hire or staff augmentation through a vetted talent network offers speed, cost leverage, and flexibility, especially for scale ups or regulated work where domain knowledge must be proven but local candidates are scarce.
  • Full scope outsourcing brings risk of misalignment, lower visibility, vendor quality variance, and rework costs.

There are over 1,000 embedded software engineer jobs open in the United States at any given time, and a limited talent pool exists for embedded systems engineers with real time experience. Understanding which model fits your strategic risk tolerance and budget before you start hiring prevents wasted cycles.

Build a Precision Profile, Not a Generic Job Spec

Generic job descriptions attract generic candidates. To source and evaluate elite embedded talent, your profile must function as a precision instrument:

  1. Core Outcome and Mission. What exact business problem must this hire solve? For example: "reduce firmware defect escape by 80% in the next release," "implement an OTA update system with rollback under real world connectivity failures," or "cut power consumption in device sleep mode by 50%." Ground the role in measurable outcomes, not vague responsibility lists.
  2. Technical Stack Reality. Specify hardware platforms (MCU families, SoC architectures), RTOS type (bare metal, FreeRTOS, Zephyr, embedded Linux), communication protocols (Wi Fi, BLE, CAN, wireless technologies), toolchains, build systems, and test harnesses. Familiarity with specific microcontrollers and tools is important for embedded systems roles. If you operate in a regulated industry like automotive or medical, state the applicable safety standards (MISRA C, IEC 62304, ISO 26262). Domain knowledge is critical when hiring for compliance heavy industries.
  3. Decision Making Authority. How much can the engineer shape firmware architecture, lead design decisions, influence choice of platforms or standards? Are they expected to define interfaces, constrain tradeoffs, and own module integrity? Or do they collaborate with an existing principal engineer? Be honest. Ambiguity here is a top reason senior candidates walk away.
  4. Growth Trajectory and Exposure. Can the hire grow into a principal or systems architect role? Will they mentor others? Are there opportunities to work across hardware, software, cloud, and integration boundaries? Senior embedded software developers evaluate your company as much as you evaluate them. Visibility and influence over the product roadmap attract the best.
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A Rigorous Approach to Vetting and Onboarding

A Battle Tested Framework for Evaluating Embedded Talent

Your candidate pipeline must minimize risk. Traditional resumes plus superficial technical quizzes are inadequate for embedded software development roles where the wrong hire can introduce firmware defects that cost millions in recalls or field failures.

The Sourcing Reality Most Companies Ignore

A resume is a weak signal in embedded hiring. Embedded software developers often come from electrical or software engineering backgrounds, sometimes with a computer science degree, sometimes without one, and the best candidates are frequently passive. They are not scrolling job boards in San Diego or Austin; they are deep in a bring up cycle or debugging a field failure.

Best sources are prescreened networks of embedded engineers, referrals from existing senior staff, and partner oriented hiring agencies focused on deeply technical hiring. Through our embedded firmware engineering network, SoftDoes maintains a vetted bench of senior embedded talent with proven production shipping records. You want to signal high expertise early in the process and screen for actual track record: production firmware shipped, hardware bring up completed, and work under tight resource constraints.

The Technical Evaluation Pipeline That Actually Works

Forget trivia questions about bit manipulation. Here is what a real technical evaluation pipeline looks like:

  • Live problem solving, not textbook quizzes. Give a candidate a real scenario: "Given a new MCU with limited flash and RAM, design a driver and communication stack for periodic sensor data plus OTA updates. Show module boundaries, scheduling, interrupt limits, and error recovery." Practical technical assessments should test constrained coding and hardware interactions for embedded developers. Proficiency in embedded C and C++ programming is non negotiable.
  • Architecture review with tradeoff analysis. Ask the candidate to walk through choosing between an RTOS and embedded Linux for a specific use case. Press on power versus performance, safety versus cost, scalability versus simplicity. Candidates should understand the entire path from electrical signals to product behavior.
  • Debugging under pressure. Present logs from an intermittent failure. Ask them to hypothesize root causes, describe how to instrument a test, and design a fix. Embedded developers need proficiency with debugging tools like oscilloscopes and logic analyzers. This reveals whether they can operate in the real world where hardware misbehaves and documentation lies.
  • Communication under pressure. Can they explain technical tradeoffs to hardware teams, product managers, and safety stakeholders? How do they write failure post mortems? How do they handle unknowns? This is the skill that separates someone who can collaborate across an organization from someone who hides behind code.

Use scorecards with weighted dimensions: technical knowledge (C, low level, RTOS), debugging and testing competence, architectural capability, domain safety and security awareness, and ability to communicate tradeoffs.

A Frictionless Ramp Up Protocol for the First 90 Days

Even the best hire fails without structured onboarding. Hiring embedded software developers requires balancing software engineering skills with hardware knowledge, and that balance takes deliberate ramp up support.

  • Pre boarding (before Day 1). Deliver a written 30/60/90 day plan upon signing. Set up all access, tools, accounts, hardware kits, and documentation. Assign a buddy or mentor. Arrange introductions to the hardware team, product managers, and key stakeholders.
  • Days 1 through 30. Focus on environment setup, codebase familiarization, and a minor fix or small bug that touches real hardware or the test harness. First PR, first firmware build, first bring up on board. Clear visibility into build toolchain, version control, and coding guidelines. This is where familiarity with your specific platform and processes develops.
  • Days 31 through 60. Assign more complex features or drivers. Allow more independence. Expect peer code reviews and cross functional collaboration with hardware designers, product, safety, and cloud teams. Frequent check ins to identify friction early.
  • Days 61 through 90. Full ownership of a module or end to end feature including design, implementation, testing, and deployment. Identify and resolve technical debt. Begin mentoring junior or mid level engineers. Propose improvements to test frameworks, observability, or deployment pipelines. At the end of Day 90, the engineer should have delivered measurable impact and a retrospective memo of learnings.

How to Make the Final Hiring Decision

Interview Signals That Predict Success or Failure

After the technical evaluation, you need to make a call. Here is what to watch for:

Red flags that should stop the process:

  • Tool obsession over problem solving. A candidate fixated on their preferred IDE or compiler rather than discussing latency, reliability, or hardware constraints is optimizing for comfort, not outcomes.
  • Inability to discuss past failures. If every project went perfectly, you are talking to someone who either lacks depth or lacks honesty. Real embedded software development involves field failures, silicon errata, and painful debugging sessions. Demand specifics.
  • Over engineering without cost awareness. Abstractions layered without regard to code size, complexity, or maintainability signal an engineer who will create technical debt rather than reduce it. Tradeoff sensitivity is a core embedded skill.
  • Vague communication under questioning. Difficulty explaining why they chose one approach over another, especially in performance, safety, or efficiency tradeoffs, is a disqualifying signal. They will struggle to collaborate with your broader team.

Green flags that indicate a multiplier:

  • Pragmatic tradeoff analysis. They can explain why they chose an RTOS over bare metal for a specific project, why a particular driver design, why a specific energy and performance compromise. They think in constraints, not ideals.
  • Data driven system thinking. They talk about error rates in the field, MTTR, power draw measurements, memory usage profiles. They measure and test rather than guess. Reliability and stability are outcomes they engineer, not hope for.
  • Proactive risk identification. They see what could go wrong before it does: failure modes, safety gaps, security exposure. They design for fallback and recovery. Knowledge of industry safety standards like MISRA C is a strong positive indicator.
  • Clear ownership of past work. They can describe specific modules they owned, how they improved them, how they influenced architecture or standards, and how they mentored or taught others. This is the difference between someone who was on a team and someone who made the team better.

Why Engineering Leaders Partner with SoftDoes

Traditional recruiting for embedded roles is slow, expensive, and high risk. Embedded software engineer salaries range from $120,000 to $145,000 annually as a base, but the total cost of employment often runs 30% to 40% higher when you factor in benefits, taxes, recruiting fees, tooling, and onboarding overhead. And that assumes you find the right person on the first attempt.

SoftDoes exists to eliminate that risk. As a North America focused custom software engineering and data and AI partner serving clients across the US and Canada, we provide:

  • Battle tested senior talent with proven embedded systems experience across automotive, medical devices, IoT, robotics, and industrial automation. Not consultants who need to learn your domain. Engineers who have shipped production firmware, performed board bring up, and debugged field failures under pressure.
  • Engineering led delivery oversight. Every engagement is managed with technical leadership, not left as an unmanaged freelancer relationship. Code quality, real time performance, and hardware integration standards are maintained by our architects.
  • Rapid deployment capability. Our vetted bench means you can have a qualified embedded firmware engineer contributing to your project in days, not the three to six months a traditional hire demands.
  • Flexible scalability. Scale your team up or down based on project phases and budget cycles without the friction of full time hiring and termination.
  • Zero risk replacement guarantee. If a hire does not meet agreed standards within a specified period, we replace them at no added cost. Your project keeps moving.

Your Next Step

Every week without the right embedded software developer on your team is a week of compounding risk: delayed launches, accumulating technical debt, and competitors moving faster. The niche market for qualified embedded talent is only getting tighter.

Stop treating embedded hiring as an HR workflow. Treat it as an engineering investment decision with direct P&L impact.

Book a technical discovery session with SoftDoes architects. We will assess your firmware challenges, map the right engagement model, and present vetted candidates who can deliver production impact within their first quarter.

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