Test Engineer Jobs in California
Test Engineer jobs in California are among the most active in the country, concentrated in aerospace and defense, semiconductor manufacturing, consumer electronics, and enterprise software across experience levels from entry-level QA associate through principal and staff engineer. The largest hiring metros are the San Francisco Bay Area, greater Los Angeles, and San Diego, where companies like Apple, Qualcomm, and Northrop Grumman maintain major engineering operations. The most sought-after specializations include hardware validation, software QA automation, and systems integration testing. Find a role that fits below and apply directly.
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Job Description:
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
The Hivemind Software Engineering Integration and Test team is seeking a Staff Automated Test Engineer to provide technical leadership for automated verification across our next-generation autonomy platform. You will define and evolve test architecture, validation infrastructure, MLOps quality systems, and CI/CD pipelines that enable the Hivemind software ecosystem to create, test, and deploy resilient autonomy capabilities for unmanned aircraft and robotic platforms operating in complex, contested, and GPS-denied environments.
You will work across production flight code, machine learning models, simulation and synthetic environments, mission planning and orchestration systems, operator-facing Ground Control Station applications, telemetry and data pipelines, cloud-native developer infrastructure, and hardware-in-the-loop systems.
In this hands-on Staff role, you will lead complex, cross-functional initiatives, establish automation and verification standards, identify systemic quality risks, and develop scalable test infrastructure. The ideal candidate combines deep Python automation and distributed-systems testing experience with technical leadership and experience validating machine learning systems throughout the data, training, evaluation, deployment, and monitoring lifecycle.
What you'll do:
- Own the technical strategy, architecture, and roadmap for automated testing, verification, and MLOps quality across the Hivemind ecosystem.
- Design and maintain scalable test frameworks for autonomy software, backend services, APIs, operator-facing applications, and distributed hardware environments.
- Lead functional, integration, regression, system, performance, reliability, and end-to-end testing across simulation, edge-compute, software-in-the-loop, and hardware-in-the-loop environments.
- Build automated ML validation pipelines covering data quality, training reproducibility, model accuracy, robustness, regression, latency, resource utilization, and system integration.
- Establish CI/CD and continuous training workflows that provide versioning and traceability for datasets, models, configurations, evaluation results, and deployment artifacts.
- Develop scenario-based validation for autonomy models, including edge cases, degraded sensing or communications, distribution shifts, and representative mission conditions.
- Create observability, analytics, and failure-triage capabilities for software behavior, model and data drift, inference health, test results, and production performance.
- Build Python automation that improves test execution, parallelization, reporting, environment setup, experiment comparison, and developer productivity.
- Create test harnesses, simulators, stubs, mocks, and synthetic data capabilities that improve system testability and coverage.
- Collaborate with software, autonomy, machine learning, data, simulation, and systems engineers to define verification strategies and improve designs before implementation.
- Develop and govern AI-assisted engineering workflows using coding agents and LLM-based tools for test generation, log analysis, debugging, and failure triage while maintaining security, reproducibility, and traceability.
Required qualifications:
- Typically 8+ years of relevant experience in software engineering, test infrastructure, developer tooling, MLOps, systems integration, or systems verification, or an equivalent combination of experience and demonstrated impact.
- 5+ years of experience building scalable automation frameworks or developer tooling in Python.
- Demonstrated success designing test, CI/CD, or MLOps infrastructure used across multiple engineering teams.
- Experience validating machine learning systems across the data, training, evaluation, packaging, deployment, and monitoring lifecycle.
- Understanding of ML quality risks such as data leakage, training-serving skew, nondeterminism, distribution shift, drift, model regression, and statistical acceptance criteria.
- Experience defining model-performance baselines, automated evaluation suites, release thresholds, and candidate-to-production comparison workflows.
- Experience testing GPU-accelerated infrastructure and workloads, including GPU scheduling, allocation, utilization, and resource contention in Kubernetes environments.
- Experience with performance benchmarking, profiling, and observability for GPU workloads, including identifying compute, memory, storage, networking, and data-loading bottlenecks.
- Experience validating multi-tenant Kubernetes environments, including RBAC, resource quotas, workload isolation, and scheduling behavior.
- Experience qualifying integrated hardware and software systems, including automated validation of compute, GPU, storage, networking, drivers, firmware, and deployed software configurations.
- Strong system-design skills and experience testing distributed systems, backend services, APIs, and integrated hardware and software environments.
- Experience developing integration and regression strategies for internally developed, third-party, open-source, and partner software, including dependency management, compatibility testing, and upgrades.
- Strong understanding of asynchronous and concurrent Python programming for scalable automation and parallel test execution.
- Experience with package and dependency management, reproducible environments, and build systems such as Conan, pip, setuptools, Poetry, Nix, or similar.
- Experience with automated observability, log collection, analytics, reporting, and root-cause analysis in complex software, data, and infrastructure systems.
- Experience working in Linux-based development environments.
Preferred qualifications:
- Experience with model registries, experiment tracking, dataset or feature versioning, model serving, and automated artifact promotion using MLflow, Kubeflow, Weights & Biases, SageMaker, Vertex AI, or similar platforms.
- Experience with GPU scheduling and orchestration platforms such as Run:ai, NVIDIA GPU Operator, KAI Scheduler, Kueue, Volcano, or similar technologies.
- Experience with NVIDIA GPU infrastructure, including CUDA, drivers, container runtimes, Multi-Instance GPU, GPU fractionalization, and hardware/software compatibility testing.
- Experience with GPU profiling and performance-analysis tools such as NVIDIA Nsight, PyTorch Profiler, or similar technologies.
- Experience validating perception, planning, decision-making, reinforcement learning, or other autonomy models in simulation and on deployed systems.
- Experience testing models on embedded or edge-compute platforms, including latency, memory, power, accelerator compatibility, quantization, and hardware-specific behavior.
- Experience qualifying production servers or appliances, including hardware validation, burn-in, provisioning, firmware, networking, storage, and software-stack validation before deployment.
- Experience with reliability, fault-injection, and recovery testing across distributed compute, storage, networking, and GPU infrastructure.
- Experience validating reproducible installation, operation, upgrades, and rollback in cloud, on-premises, disconnected, or air-gapped environments.
- Experience with containers, Kubernetes, cloud infrastructure, infrastructure as code, and reproducible test environments.
- Proficiency with Go or TypeScript for automation tooling or UI test development.
- Experience integrating Python with native C or C++ applications through bindings, wrappers, subprocess interfaces, or similar interoperability tooling.
- Aerospace, robotics, autonomy, embedded systems, or safety-critical software experience.
- Familiarity with software-in-the-loop, hardware-in-the-loop, requirements-based verification, configuration management, artifact traceability, or standards such as DO-178C and MIL-STD-882.
Full-time regular employee offer package:
Job Description:
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
The Hivemind Software Engineering Integration and Test team is seeking a Staff Automated Test Engineer to provide technical leadership for automated verification across our next-generation autonomy platform. You will define and evolve test architecture, validation infrastructure, MLOps quality systems, and CI/CD pipelines that enable the Hivemind software ecosystem to create, test, and deploy resilient autonomy capabilities for unmanned aircraft and robotic platforms operating in complex, contested, and GPS-denied environments.
You will work across production flight code, machine learning models, simulation and synthetic environments, mission planning and orchestration systems, operator-facing Ground Control Station applications, telemetry and data pipelines, cloud-native developer infrastructure, and hardware-in-the-loop systems.
In this hands-on Staff role, you will lead complex, cross-functional initiatives, establish automation and verification standards, identify systemic quality risks, and develop scalable test infrastructure. The ideal candidate combines deep Python automation and distributed-systems testing experience with technical leadership and experience validating machine learning systems throughout the data, training, evaluation, deployment, and monitoring lifecycle.
What you'll do:
- Own the technical strategy, architecture, and roadmap for automated testing, verification, and MLOps quality across the Hivemind ecosystem.
- Design and maintain scalable test frameworks for autonomy software, backend services, APIs, operator-facing applications, and distributed hardware environments.
- Lead functional, integration, regression, system, performance, reliability, and end-to-end testing across simulation, edge-compute, software-in-the-loop, and hardware-in-the-loop environments.
- Build automated ML validation pipelines covering data quality, training reproducibility, model accuracy, robustness, regression, latency, resource utilization, and system integration.
- Establish CI/CD and continuous training workflows that provide versioning and traceability for datasets, models, configurations, evaluation results, and deployment artifacts.
- Develop scenario-based validation for autonomy models, including edge cases, degraded sensing or communications, distribution shifts, and representative mission conditions.
- Create observability, analytics, and failure-triage capabilities for software behavior, model and data drift, inference health, test results, and production performance.
- Build Python automation that improves test execution, parallelization, reporting, environment setup, experiment comparison, and developer productivity.
- Create test harnesses, simulators, stubs, mocks, and synthetic data capabilities that improve system testability and coverage.
- Collaborate with software, autonomy, machine learning, data, simulation, and systems engineers to define verification strategies and improve designs before implementation.
- Develop and govern AI-assisted engineering workflows using coding agents and LLM-based tools for test generation, log analysis, debugging, and failure triage while maintaining security, reproducibility, and traceability.
Required qualifications:
- Typically 8+ years of relevant experience in software engineering, test infrastructure, developer tooling, MLOps, systems integration, or systems verification, or an equivalent combination of experience and demonstrated impact.
- 5+ years of experience building scalable automation frameworks or developer tooling in Python.
- Demonstrated success designing test, CI/CD, or MLOps infrastructure used across multiple engineering teams.
- Experience validating machine learning systems across the data, training, evaluation, packaging, deployment, and monitoring lifecycle.
- Understanding of ML quality risks such as data leakage, training-serving skew, nondeterminism, distribution shift, drift, model regression, and statistical acceptance criteria.
- Experience defining model-performance baselines, automated evaluation suites, release thresholds, and candidate-to-production comparison workflows.
- Experience testing GPU-accelerated infrastructure and workloads, including GPU scheduling, allocation, utilization, and resource contention in Kubernetes environments.
- Experience with performance benchmarking, profiling, and observability for GPU workloads, including identifying compute, memory, storage, networking, and data-loading bottlenecks.
- Experience validating multi-tenant Kubernetes environments, including RBAC, resource quotas, workload isolation, and scheduling behavior.
- Experience qualifying integrated hardware and software systems, including automated validation of compute, GPU, storage, networking, drivers, firmware, and deployed software configurations.
- Strong system-design skills and experience testing distributed systems, backend services, APIs, and integrated hardware and software environments.
- Experience developing integration and regression strategies for internally developed, third-party, open-source, and partner software, including dependency management, compatibility testing, and upgrades.
- Strong understanding of asynchronous and concurrent Python programming for scalable automation and parallel test execution.
- Experience with package and dependency management, reproducible environments, and build systems such as Conan, pip, setuptools, Poetry, Nix, or similar.
- Experience with automated observability, log collection, analytics, reporting, and root-cause analysis in complex software, data, and infrastructure systems.
- Experience working in Linux-based development environments.
Preferred qualifications:
- Experience with model registries, experiment tracking, dataset or feature versioning, model serving, and automated artifact promotion using MLflow, Kubeflow, Weights & Biases, SageMaker, Vertex AI, or similar platforms.
- Experience with GPU scheduling and orchestration platforms such as Run:ai, NVIDIA GPU Operator, KAI Scheduler, Kueue, Volcano, or similar technologies.
- Experience with NVIDIA GPU infrastructure, including CUDA, drivers, container runtimes, Multi-Instance GPU, GPU fractionalization, and hardware/software compatibility testing.
- Experience with GPU profiling and performance-analysis tools such as NVIDIA Nsight, PyTorch Profiler, or similar technologies.
- Experience validating perception, planning, decision-making, reinforcement learning, or other autonomy models in simulation and on deployed systems.
- Experience testing models on embedded or edge-compute platforms, including latency, memory, power, accelerator compatibility, quantization, and hardware-specific behavior.
- Experience qualifying production servers or appliances, including hardware validation, burn-in, provisioning, firmware, networking, storage, and software-stack validation before deployment.
- Experience with reliability, fault-injection, and recovery testing across distributed compute, storage, networking, and GPU infrastructure.
- Experience validating reproducible installation, operation, upgrades, and rollback in cloud, on-premises, disconnected, or air-gapped environments.
- Experience with containers, Kubernetes, cloud infrastructure, infrastructure as code, and reproducible test environments.
- Proficiency with Go or TypeScript for automation tooling or UI test development.
- Experience integrating Python with native C or C++ applications through bindings, wrappers, subprocess interfaces, or similar interoperability tooling.
- Aerospace, robotics, autonomy, embedded systems, or safety-critical software experience.
- Familiarity with software-in-the-loop, hardware-in-the-loop, requirements-based verification, configuration management, artifact traceability, or standards such as DO-178C and MIL-STD-882.
Full-time regular employee offer package:
See All 917+ Test Engineer Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
Find Test Engineer JobsTest Engineer Jobs by City in California
Where California roles are concentrated, by current openings.
Test Engineer Job Market in California
A snapshot from current California openings, updated as new roles post.
Who's Hiring
- Tesla60
- Northrop Grumman51
- Anduril32
- Apple26
- Qualcomm19
Top Industries Hiring
- Technology & Software55
- Electronics & Hardware35
- Manufacturing26
- Aerospace & Defense12
- Automotive11
What California Employers Look For
The qualifications that appear most often in test engineer jobs across California.
- Bachelor's degree in electrical engineering, computer science, or a related engineering field
- Hands-on experience writing and executing test plans, cases, and automated test scripts
- Proficiency with test automation frameworks such as Selenium, pytest, or similar tools
- Experience with bug tracking and version control tools including Jira and Git
- Familiarity with hardware validation, signal analysis equipment, or embedded systems testing
- Strong written communication skills for documenting defects, test results, and reports
Test Engineer Jobs in California: Frequently Asked Questions
How do you become a test engineer in California?
Most test engineer roles in California require a bachelor's degree in electrical engineering, computer science, mechanical engineering, or a closely related field. California does not require a state-issued license specifically for test engineers, though roles in aerospace, defense, or safety-critical systems may require a Professional Engineer license from the California Board for Professional Engineers, Land Surveyors, and Geologists. Building proficiency in test automation and earning industry certifications such as ISTQB strengthens your candidacy.
Which companies hire test engineers in California?
Employers hiring test engineers in California right now include Tesla, Northrop Grumman, and Anduril, based on current listings on Migrate Mate as of September 2026. California's concentration of aerospace primes, semiconductor firms, and large consumer technology companies means test engineer demand stays broad and consistent across the state.
Which California cities have the most test engineer jobs?
San Diego, San Jose, and Santa Clara have the most test engineer openings in California. The Bay Area leads due to its dense concentration of semiconductor and software companies, while Los Angeles and San Diego are driven by aerospace, defense contractors, and hardware manufacturers with large validation and quality engineering teams.
Are there remote test engineer jobs in California?
Yes, but they're limited. About 34% of test engineer openings tied to California are remote or hybrid as of September 2026, reflecting that much of the work involves physical hardware, lab equipment, or on-site test environments. Software QA and test automation roles are the most likely to offer remote or hybrid arrangements within the field.
How can I get hired as a test engineer in California with little or no experience?
The most realistic entry path is an associate or junior QA role at a mid-size software company in the Bay Area or Los Angeles, where teams regularly hire new graduates. Large California employers like Apple, Intel, and Northrop Grumman run structured new-grad and early-career engineering programs. Moving laterally from a QA analyst or technical support role is also common. Completing an ISTQB Foundation certification and building a portfolio of automated test scripts on GitHub gives candidates a clear edge over those without formal experience.
Where can I find and apply to test engineer jobs in California?
You can find and apply to test engineer jobs in California on Migrate Mate, which lists current California openings from employers across the state. Find the roles that fit your experience and specialization and apply directly from the listing.
See All 917+ Test Engineer Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
Find Test Engineer Jobs