Diagnostic Engineer Jobs
Diagnostic Engineer jobs are open across automotive, medical devices, aerospace, and industrial manufacturing, from entry-level to senior and principal roles, with specializations in embedded systems diagnostics, fault analysis, and test automation. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 31+ Diagnostic Engineer jobs
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 7 years of experience leading technical project strategy, ML design, and end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
- 5 years of experience with design and architecture; and testing/launching software products.
- 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- Experience in designing, implementing and troubleshooting hardware diagnostics.
Preferred qualifications:
- Master’s degree in Computer Science, Electrical/Computer Engineering, Artificial Intelligence, or equivalent practical experience.
- 10 years of professional software engineering and machine learning experience, including technical leadership on infrastructure systems.
- 8 years of experience with data structures/algorithms.
- Experience building production machine learning systems, agentic architectures, LLM pipelines, or deep learning models applied to system telemetry.
- Advanced experience in C++ or Rust, along with experience using machine learning frameworks and data processing tools.
- Knowledge of computer architecture, modern ASIC/SoC functional blocks, high-speed interfaces, and hardware failure mechanisms.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
The Platforms Infrastructure Engineering Core Systems and Capacity Assurance team is responsible for delivering the tools and systems that manage the state of a machine throughout it's service lifetime while part of the Google Fleet. We are also responsible for the diagnostic tests, and repair strategies to ensure that a machine is healthy and ready to serve. This includes software installation, hardware health testing, and machine management. We are responsible for the validation of machine health at the time of manufacture and also when it is deployed in the data center.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Serve as the technical authority driving the convergence of Artificial Intelligence, silicon/hardware design analysis, and systems engineering.
- Architect and build generative AI tools, foundation model skills, and agentic workflows by leveraging complex ASIC architectural specifications, system schematics, software contracts, and historical test execution telemetry.
- Transform how hardware diagnostic software is created, optimized, and deployed across Google’s next-generation compute, accelerator (TPU/GPU), network, and storage infrastructure.
- Empower Diagnostic Software Engineers to achieve comprehensive test coverage across all hardware components, interfaces, and functional blocks.
- Optimize test execution time and pinpoint optimal repair actions using advanced artificial intelligence systems.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 7 years of experience leading technical project strategy, ML design, and end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
- 5 years of experience with design and architecture; and testing/launching software products.
- 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- Experience in designing, implementing and troubleshooting hardware diagnostics.
Preferred qualifications:
- Master’s degree in Computer Science, Electrical/Computer Engineering, Artificial Intelligence, or equivalent practical experience.
- 10 years of professional software engineering and machine learning experience, including technical leadership on infrastructure systems.
- 8 years of experience with data structures/algorithms.
- Experience building production machine learning systems, agentic architectures, LLM pipelines, or deep learning models applied to system telemetry.
- Advanced experience in C++ or Rust, along with experience using machine learning frameworks and data processing tools.
- Knowledge of computer architecture, modern ASIC/SoC functional blocks, high-speed interfaces, and hardware failure mechanisms.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
The Platforms Infrastructure Engineering Core Systems and Capacity Assurance team is responsible for delivering the tools and systems that manage the state of a machine throughout it's service lifetime while part of the Google Fleet. We are also responsible for the diagnostic tests, and repair strategies to ensure that a machine is healthy and ready to serve. This includes software installation, hardware health testing, and machine management. We are responsible for the validation of machine health at the time of manufacture and also when it is deployed in the data center.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Serve as the technical authority driving the convergence of Artificial Intelligence, silicon/hardware design analysis, and systems engineering.
- Architect and build generative AI tools, foundation model skills, and agentic workflows by leveraging complex ASIC architectural specifications, system schematics, software contracts, and historical test execution telemetry.
- Transform how hardware diagnostic software is created, optimized, and deployed across Google’s next-generation compute, accelerator (TPU/GPU), network, and storage infrastructure.
- Empower Diagnostic Software Engineers to achieve comprehensive test coverage across all hardware components, interfaces, and functional blocks.
- Optimize test execution time and pinpoint optimal repair actions using advanced artificial intelligence systems.
Diagnostic Engineer Jobs by Experience Level
See All 31 Diagnostic Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Diagnostic Engineer JobsDiagnostic Engineer Job Market
Who's Hiring
Top Industries Hiring
- Healthcare & Medical Services
- Technology & Software
What Employers Look For
The qualifications that appear most often in diagnostic engineer jobs.
- Bachelor's degree in electrical, mechanical, or systems engineering or a related field
- Hands-on experience with diagnostic tools such as oscilloscopes, CANalyzer, or OBD-II scanners
- Proficiency in test automation scripting using Python, MATLAB, or LabVIEW
- Familiarity with FMEA, root cause analysis, and fault tree analysis methodologies
- Experience reading and interpreting schematics, wiring diagrams, and technical specifications
- Strong written communication skills for documenting test procedures and defect reports
Tips for Your Diagnostic Engineer Job Search
Tailor your resume to fault diagnostics
Hiring managers scan for specific tools and methodologies, so call out your experience with FMEA, root cause analysis, and diagnostic software like CANalyzer or LabVIEW by name. Generic engineering resumes get filtered out before a human reads them.
Apply early to roles that fit
Migrate Mate lists diagnostic engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target job titles that map to your domain
Job postings for this role appear under several titles including validation engineer, test systems engineer, and field service engineer. Searching only 'diagnostic engineer' means you miss a large portion of matching openings in automotive OEM and medical device companies.
Quantify test coverage and defect outcomes
Interviewers want to see the impact of your diagnostic work, not just your methods. Replace 'conducted system testing' with specific outcomes like defect catch rates, reduction in field failures, or test cycle time you cut on a past project.
Prepare a structured technical walkthrough
Most diagnostic engineer interviews include a live fault-analysis scenario or take-home case. Practice walking through a real fault you diagnosed, covering how you isolated variables, the tools you used, and what the resolution prevented downstream.
Negotiate using total compensation, not just base
Diagnostic roles in automotive and aerospace often include on-call premiums, tool allowances, and shift differentials that vary widely by employer. Before accepting any offer, ask the recruiter to break down the full compensation package so you're comparing equivalent totals.
Diagnostic Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most diagnostic engineers?
The companies hiring the most diagnostic engineers right now include Tesla, Rivian, and Banner Health, with the largest share of openings in California, Colorado, and South Carolina, based on current listings on Migrate Mate as of November 2026. Automotive OEMs, medical device manufacturers, and aerospace suppliers consistently account for the highest volume of postings.
How many diagnostic engineer jobs are remote?
About 17% of diagnostic engineer openings are fully remote or hybrid as of November 2026, reflecting the hands-on nature of most hardware-facing roles. Sub-areas with the strongest remote availability include software-side diagnostic tool development, test automation engineering, and data analysis for field diagnostic systems.
How do you become a diagnostic engineer?
Start with a bachelor's degree in electrical, mechanical, or systems engineering, then build hands-on experience with diagnostic hardware and test software through internships, co-ops, or entry-level validation roles. Develop fluency in at least one scripting language for test automation, get comfortable reading schematics and interpreting fault codes, and pursue domain certifications relevant to your target industry, such as automotive embedded systems or medical device quality standards.
Can you get a diagnostic engineer job with little experience?
Yes, entry-level diagnostic engineer roles exist and typically look for candidates who can demonstrate hands-on troubleshooting ability even without full professional experience. Build a portfolio of personal or academic projects that show fault isolation, test bench work, or scripted diagnostics. Applying to validation engineer or test technician roles first is a common path, since those positions build the diagnostic skills that employers expect at the engineer level.
What does the diagnostic engineer interview process look like?
Most diagnostic engineer interviews follow a multi-stage format: an initial recruiter screen, a technical phone or video interview covering diagnostics methodology and tool experience, and a final round that often includes a live fault-analysis exercise or take-home case study. Some employers add a practical bench test or ask candidates to walk through a past diagnostic project in detail, so prepare a structured example of a fault you identified, isolated, and resolved.
Where can I find and apply to diagnostic engineer jobs?
You can find and apply to diagnostic engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Search the listings for roles that match your domain and experience level, then apply directly to each one that fits.
See All 31 Diagnostic Engineer Jobs
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