Automation Engineer Jobs in California
Automation Engineer jobs in California are among the most active in the country, concentrated in semiconductor fabrication, defense manufacturing, food and beverage processing, and life sciences, with openings at every level from entry-level controls technician to senior systems architect. The heaviest hiring happens in the San Francisco Bay Area, Los Angeles, and San Diego, where companies like Northrop Grumman, Applied Materials, and Abbott Laboratories maintain large engineering operations. The most in-demand specialties are PLC programming, robotics integration, and industrial controls for cleanroom and regulated manufacturing environments. 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 348+ Automation Engineer Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
Find Automation Engineer JobsAutomation Engineer Jobs by City in California
Where California roles are concentrated, by current openings.
Automation Engineer Job Market in California
A snapshot from current California openings, updated as new roles post.
Who's Hiring
- Apple23
- Anduril18
- Tesla10
- SpaceX7
- E-Technologies Group6
Top Industries Hiring
- Technology & Software24
- Electronics & Hardware10
- Consulting & Professional Services8
- Construction & Real Estate4
- Biotechnology & Pharmaceuticals4
What California Employers Look For
The qualifications that appear most often in automation engineer jobs across California.
- Bachelor's degree in electrical, mechanical, or mechatronics engineering or a closely related field
- Hands-on experience programming PLCs using platforms such as Allen-Bradley or Siemens
- Proficiency with robotics integration and automated assembly systems in a manufacturing environment
- Familiarity with FDA or aerospace quality standards such as GMP, AS9100, or ISO 9001
- Experience with SCADA systems, HMI configuration, and industrial network protocols like EtherNet/IP
- Strong troubleshooting skills for electromechanical systems and motion control equipment
Automation Engineer Jobs in California: Frequently Asked Questions
How do you become a automation engineer in California?
Most automation engineer roles in California require a bachelor's degree in electrical, mechanical, or mechatronics engineering from an accredited program. California does not require a state-issued license specifically for automation engineers, though a Professional Engineer license issued through the California Board for Professional Engineers, Land Surveyors, and Geologists strengthens candidacy for senior or project-lead roles. Certifications from organizations like PMMI or vendor-specific PLC credentials from Rockwell or Siemens are common additional qualifications employers look for.
How much do automation engineers make in California?
Automation engineers in California earn a median of about $130,850 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $74,950 for the lowest 10% to over $212,130 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire automation engineers in California?
Employers hiring automation engineers in California right now include Apple, Anduril, and Tesla, based on current listings on Migrate Mate as of September 2026. California's concentration of semiconductor, aerospace, and life sciences manufacturers means demand is especially consistent at large facilities with ongoing capital equipment and production line projects.
Which California cities have the most automation engineer jobs?
San Diego, San Francisco, and San Jose have the most automation engineer openings in California. The Bay Area dominates because of its density of semiconductor fabs and advanced manufacturing campuses, while Los Angeles draws heavily from aerospace and defense contractors, and San Diego's life sciences and defense presence anchors demand in the south.
Are there remote automation engineer jobs in California?
Yes, but they're rare. Automation engineering is fundamentally hands-on work tied to physical equipment, so most roles require on-site presence. About 36% of automation engineer openings tied to California are remote or hybrid as of September 2026, and those openings tend to be in controls software design, simulation, or project management rather than direct equipment commissioning or maintenance.
How can I get hired as a automation engineer in California with little or no experience?
The most realistic entry path is applying to associate or junior controls engineer roles at large California manufacturers that run structured new-graduate programs, including companies in the Bay Area semiconductor sector and Southern California aerospace industry. Internships through California State University or UC system engineering programs placed at these facilities are a common bridge. Building a portfolio around PLC ladder logic projects or completing a Rockwell Automation certification gives candidates a measurable edge over applicants with no hands-on credentials.
Where can I find and apply to automation engineer jobs in California?
You can find and apply to automation engineer jobs in California on Migrate Mate, which lists current California openings. Search the available roles, find the ones that fit your experience and location, and apply directly.
See All 348+ Automation Engineer Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
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