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Systems Performance Engineer Jobs in California (NOW HIRING)

Qualcomm

SOC Systems Performance Engineer

San Diego, CA · On-site

$99.40K - $149.20K/yr

Engineer market-leading SoC products with best-in-class performance * Lead and conduct performance and systems studies * Conduct or contribute to analysis to define our SOC architecture plans ...

Nvidia

Senior Systems Performance Engineer

Santa Clara, CA

$122.70K - $167.90K/yr

... engineers to develop and implement complex automated test plans for our industry leading GPU accelerated computing products. What you will be doing: * System architecture, design, performance ...

AFL

System Performance Engineer

Los Angeles, CA · On-site

... systems. This is accomplished by notify carriers and venue owners of system health and providing ... The Performance Engineer will use RF test equipment to evaluate system performance, evaluate ...

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$52.8K

$125.5K

$164.8K

How much do systems performance engineer jobs pay per year?

As of May 29, 2026, the average yearly pay for systems performance engineer in California is $125,549.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,700.00 and $154,900.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Systems Performance Engineer, and why are they important?

To thrive as a Systems Performance Engineer, you need strong analytical abilities, expertise in systems architecture, and a degree in computer science or a related field. Familiarity with performance monitoring tools (like New Relic, Dynatrace, or Splunk), scripting languages, and experience with operating systems and cloud platforms is typically required. Exceptional problem-solving skills, attention to detail, and effective communication help you collaborate across technical teams and resolve complex issues quickly. These skills ensure the optimal performance, reliability, and scalability of critical IT systems, which are vital for business continuity and user satisfaction.

What are some common challenges faced by Systems Performance Engineers in large-scale production environments?

Systems Performance Engineers often encounter challenges such as identifying bottlenecks in complex, distributed systems and dealing with unpredictable performance issues under varying workloads. They must balance optimizing system resources while ensuring minimal downtime and maintaining service reliability. Collaboration with development, operations, and QA teams is crucial to implement performance improvements and proactively address potential scalability concerns. Staying current with new technologies and monitoring tools also helps in effectively troubleshooting and tuning performance.

What are Systems Performance Engineers?

Systems Performance Engineers are professionals who analyze, monitor, and optimize the performance of computer systems and applications. They identify bottlenecks, run performance tests, and recommend improvements to ensure systems operate efficiently under varying workloads. Their role often involves collaborating with developers and IT teams to resolve issues, improve response times, and ensure scalability. These engineers use specialized tools to collect and interpret performance data, helping organizations maintain reliable and high-performing technology environments.
What are popular job titles related to Systems Performance Engineer jobs in California? For Systems Performance Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Systems Performance Engineer jobs in California look for? The top searched job categories for Systems Performance Engineer jobs in California are:
Systems Performance Engineer jobs near you
Infographic showing various Systems Performance Engineer job openings in California as of May 2026, with employment types broken down into 1% As Needed, 96% Full Time, 1% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $125,549 per year, or $60.4 per hour.
ML Systems Performance Engineer

ML Systems Performance Engineer

Cerebras Systems

Sunnyvale, CA • On-site

Full-time

Posted 7 days ago

Job description

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.
Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
About The Role
Engineers on the inference performance team operate at the intersection of hardware and software, driving end-to-end model inference speed and throughput. Their work spans low-level kernel performance debugging and optimization, system-level performance analysis, performance modeling and estimation, and the development of tooling for performance projection and diagnostics.
Responsibilities
  • Build performance models (kernel-level, end-to-end) to estimate the performance of state of the art and customer ML models.
  • Optimize and debug our kernel micro code and compiler algorithms to elevate ML model inference speed, throughput and compute utilization on the Cerebras WSE.
  • Debug and understand runtime performance on the system and cluster.
  • Develop tools and infrastructure to help visualize performance data collected from the Wafer Scale Engine and our compute cluster.
Requirements
  • Bachelors / Masters / PhD in Electrical Engineering or Computer Science.
  • Strong background in computer architecture.
  • Exposure to and understanding of low-level deep learning / LLM math.
  • Strong analytical and problem-solving mindset.
  • 3+ years of experience in a relevant domain (Computer Architecture, CPU/GPU Performance, Kernel Optimization, HPC).
  • Experience working on CPU/GPU simulators.
  • Exposure to performance profiling and debug on any system pipeline.
  • Comfort with C++ and Python.

Why Join Cerebras
People who are serious about software make their own hardware. At Cerebras we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we've reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
  1. Build a breakthrough AI platform beyond the constraints of the GPU.
  2. Publish and open source their cutting-edge AI research.
  3. Work on one of the fastest AI supercomputers in the world.
  4. Enjoy job stability with startup vitality.
  5. Our simple, non-corporate work culture that respects individual beliefs.

Read our blog: Five Reasons to Join Cerebras in 2026.
Apply today and become part of the forefront of groundbreaking advancements in AI!
Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
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