Performance Engineer Jobs in California
Performance Engineer jobs in California are highly active, with strong demand across aerospace and defense, automotive testing, semiconductor manufacturing, and enterprise software, at every level from entry-level analyst through principal engineer. The largest concentrations of openings are in the Los Angeles, San Jose, and San Diego metros, where established employers such as Northrop Grumman, Qualcomm, and Apple consistently hire performance engineers. The most sought-after specializations in California include load testing and capacity planning, embedded systems performance, and application reliability engineering. Find a role that fits below and apply directly.
Find Performance Engineer JobsOverview
Showing 5 of 164+ Performance Engineer jobs
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; New York, NY, USA.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 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.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- Experience with machine learning, compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc).
- Experience in tailoring algorithms and ML models to exploit ML accelerator architecture strengths and minimize weaknesses.
- Experience in low-level GPU programming (CUDA, OpenCL, etc.) and performance tuning techniques.
- Understanding of modern Graphics Processing Unit (GPU), TPU or other ML accelerator architectures, memory hierarchies, and performance bottlenecks.
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.
Google’s Core Machine Learning (ML) organization is seeking software engineers to join the team known for pioneering work with Tensor Processing Units (TPUs). In this role, you will work on Gemini, as well as industry leading open-source models, to understand model architecture and optimize the performance of these Machine Learning (ML) models on TPU systems for both Just After eXecution (JAX) and PyTorch platforms. You will improve the performance of ever-evolving ML workloads, achieving results. These fundamental efforts will influence next-generation (next-gen) TPU architectures via partnerships, ensuring performance for Gemini and Open-Source Software (OSS) Machine Learning (ML) models.
The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Identify and maintain ML training and serving benchmarks that are representative to Google production and broader ML industry.
- Achieve performance for customer launches, and in case of third-party/Open-Source Software (3P/OSS) models, for engaged benchmark submissions ML commons, InferenceMAX, etc.).
- Use the benchmarks to identify performance opportunities and drive out-of-the-box performance toward improving the compiler, runtime, etc., in collaboration with those teams.
- Engage with Google Product teams and researchers to solve their performance problems (e.g., onboard new ML models and products on Google new TPU hardware, enabling larger models (giant models) to train efficiently on a very large-scale (i.e., thousands of TPUs).
- Analyze performance and efficiency metrics to identify bottlenecks, design, and implement solutions at Google fleet-wide scale.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; New York, NY, USA.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 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.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- Experience with machine learning, compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc).
- Experience in tailoring algorithms and ML models to exploit ML accelerator architecture strengths and minimize weaknesses.
- Experience in low-level GPU programming (CUDA, OpenCL, etc.) and performance tuning techniques.
- Understanding of modern Graphics Processing Unit (GPU), TPU or other ML accelerator architectures, memory hierarchies, and performance bottlenecks.
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.
Google’s Core Machine Learning (ML) organization is seeking software engineers to join the team known for pioneering work with Tensor Processing Units (TPUs). In this role, you will work on Gemini, as well as industry leading open-source models, to understand model architecture and optimize the performance of these Machine Learning (ML) models on TPU systems for both Just After eXecution (JAX) and PyTorch platforms. You will improve the performance of ever-evolving ML workloads, achieving results. These fundamental efforts will influence next-generation (next-gen) TPU architectures via partnerships, ensuring performance for Gemini and Open-Source Software (OSS) Machine Learning (ML) models.
The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Identify and maintain ML training and serving benchmarks that are representative to Google production and broader ML industry.
- Achieve performance for customer launches, and in case of third-party/Open-Source Software (3P/OSS) models, for engaged benchmark submissions ML commons, InferenceMAX, etc.).
- Use the benchmarks to identify performance opportunities and drive out-of-the-box performance toward improving the compiler, runtime, etc., in collaboration with those teams.
- Engage with Google Product teams and researchers to solve their performance problems (e.g., onboard new ML models and products on Google new TPU hardware, enabling larger models (giant models) to train efficiently on a very large-scale (i.e., thousands of TPUs).
- Analyze performance and efficiency metrics to identify bottlenecks, design, and implement solutions at Google fleet-wide scale.
See All 164+ Performance Engineer Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
Find Performance Engineer JobsPerformance Engineer Jobs by City in California
Where California roles are concentrated, by current openings.
Performance Engineer Job Market in California
A snapshot from current California openings, updated as new roles post.
Who's Hiring
- Qualcomm18
- Apple17
- NVIDIA15
- Google10
- OpenAI8
Top Industries Hiring
- Technology & Software25
- Electronics & Hardware16
- Science & Research9
- Telecommunications7
- Artificial Intelligence4
What California Employers Look For
The qualifications that appear most often in performance engineer jobs across California.
- Bachelor's degree in mechanical, electrical, or software engineering or a related technical field
- Proficiency with performance testing tools such as JMeter, LoadRunner, or Gatling
- Experience designing and executing load, stress, and scalability test plans
- Familiarity with APM platforms such as Dynatrace, New Relic, or AppDynamics
- Ability to analyze system bottlenecks and deliver data-driven optimization recommendations
- Strong scripting or coding skills in Python, Java, or shell scripting for automation workflows
Performance Engineer Jobs in California: Frequently Asked Questions
How do you become a performance engineer in California?
A bachelor's degree in mechanical engineering, computer science, electrical engineering, or a related discipline is the standard entry point for performance engineer roles in California. The field does not require a California state license for most software and systems positions, but engineers working on aerospace or automotive hardware may pursue professional engineer registration through the California Board for Professional Engineers, Land Surveyors, and Geologists. Certifications in cloud platforms or performance testing tools strengthen a California application considerably.
Which companies hire performance engineers in California?
Employers hiring performance engineers in California right now include Qualcomm, Apple, and NVIDIA, based on current listings on Migrate Mate as of October 2026. California's concentration of semiconductor, defense, and enterprise software firms means hiring is broad across both hardware-focused and cloud-native performance engineering disciplines.
Which California cities have the most performance engineer jobs?
Santa Clara, San Diego, and San Jose have the most performance engineer openings in California. The San Jose area leads because of the density of semiconductor and enterprise software headquarters in Silicon Valley, while Los Angeles is driven by aerospace and defense contractors, and San Diego's activity reflects the strong presence of wireless technology firms and defense systems companies anchored there.
Are there remote performance engineer jobs in California?
Yes, and more than many engineering disciplines, because a meaningful portion of performance engineering work involves analyzing data, writing test scripts, and reviewing telemetry that can be done entirely through cloud tooling. About 44% of performance engineer openings tied to California are remote or hybrid as of October 2026, reflecting the role's strong presence in software and cloud infrastructure teams. Hardware and embedded systems positions remain predominantly on-site.
How can I get hired as a performance engineer in California with little or no experience?
The most realistic entry path is through a quality assurance or software test engineer role, which large California employers such as Qualcomm, Apple, and Northrop Grumman staff at the associate level and regularly promote into performance engineering. Building a portfolio that demonstrates load test scripts, bottleneck analysis write-ups, or open-source contributions to testing frameworks gives candidates a concrete edge. Community college certificate programs in software testing available throughout the California State University system also help bridge the gap for candidates without a four-year engineering degree.
Where can I find and apply to performance engineer jobs in California?
You can find and apply to performance engineer jobs in California on Migrate Mate, which lists current California openings across industries and experience levels. Search the listings, find roles that fit your background and location, and apply directly to the employers posting them.
See All 164+ Performance Engineer Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
Find Performance Engineer Jobs