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Performance Engine Tuning Jobs in California (NOW HIRING)

Retell AI

SEO Strategist/Growth SEO Lead

San Francisco, CA · On-site +1

$170K - $230K/yr

Instead of basic automation that needs constant human tuning, we're creating intelligent AI ... Improve site architecture, indexing, crawlability, and performance * Continuously optimize pages ...

Nastech Global

AEM Developer

Sunnyvale, CA · On-site

Optimize site structure, metadata, and content for search engine indexing and performance ... Participate in code reviews, technical design discussions, and performance tuning efforts.

CYNET SYSTEMS

AI/ML Engineer

Aliso Viejo, CA · On-site

$52 - $57/hr

Perform model fine-tuning, hyperparameter optimization, and performance enhancement. * Develop and ... recruitment engine operating across North America and Asia--ensuring speed, quality, and ...

Simarn Solutions, LLC

Sr. Cloud Engineer

Redwood City, CA · On-site

$68.25 - $91.25/hr

... performance tuning, debugging, metrics measurement, security, resilient design and disaster ... show engine innodb status;" and working knowledge of admin tools such as monyog. * The ability to ...

Walmart

Staff, Software Engineer

Cupertino, CA · On-site

$143K - $286K/yr

... orchestration engine. Advanced Retry Policies: Knowledge of jitter, exponential backoff, and ... Strong production debugging and performance tuning skills. Preferred Qualifications * Experience in ...

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Performance Engine Tuning information

See California salary details

$108.1K

$123.4K

$137.2K

How much do performance engine tuning jobs pay per year?

As of Aug 5, 2026, the average yearly pay for performance engine tuning in California is $123,363.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,000.00 and $130,800.00 per year, depending on experience, location, and employer.

What does a performance engine tuning specialist do?

A typical day for a Performance Engine Tuning specialist involves assessing customer vehicles, performing engine diagnostics, calibrating ECU settings, and utilizing dynamometer testing equipment to validate performance improvements. You may collaborate closely with other technicians, service advisors, and occasionally directly with clients to discuss performance goals and modifications. The role requires a blend of hands-on adjustments and computer-based tuning, with an emphasis on both technical expertise and precision. Over time, you may also be involved in researching new tuning techniques and staying updated with advancements in automotive technology to ensure optimal results for a wide range of vehicles.

What is performance engine tuning?

A Performance Engine Tuning job involves optimizing an engine's performance by adjusting parameters such as fuel injection, ignition timing, and air-to-fuel ratio. Tuners use specialized software and tools to modify the engine control unit (ECU) for better horsepower, torque, and efficiency. This role requires knowledge of engine mechanics, diagnostics, and aftermarket modifications. Tuners work with various vehicles, from daily drivers to high-performance race cars, ensuring they run efficiently and safely.

What skills and qualifications are needed for performance engine tuning?

To thrive in Performance Engine Tuning, you need a solid background in automotive mechanics, internal combustion engine theory, and hands-on experience with diagnostic and tuning procedures. Familiarity with engine management systems, ECU programming tools, and performance software (such as Dynojet or HP Tuners) is typically required, along with relevant technical certifications. Strong analytical thinking, attention to detail, and effective communication are valuable soft skills in this field. These competencies are crucial for accurately optimizing engine performance, troubleshooting issues, and conveying technical information to clients or team members.

What are popular job titles related to Performance Engine Tuning jobs in California? For Performance Engine Tuning jobs in California, the most frequently searched job titles are:
What job categories do people searching Performance Engine Tuning jobs in California look for? The top searched job categories for Performance Engine Tuning jobs in California are:
Performance Engine Tuning jobs near you
Infographic showing various Performance Engine Tuning job openings in California as of July 2026, with employment types broken down into 91% Full Time, and 9% Contract. Highlights an 100% In-person job distribution, with an average salary of $123,363 per year, or $59.3 per hour.

Member of Technical Staff - Developer Technology

RadixArk

Palo Alto, CA • On-site

Full-time

Posted 12 days ago

Job description

About the Role
RadixArk is seeking a Member of Technical Staff, Developer Technology (DevTech) to make LLM inference and training dramatically faster, cheaper, and more accessible on modern GPU hardware. Our systems sit at the center of how modern AI is served and trained: SGLang is a high-performance inference engine that serves trillions of tokens daily across leading AI companies and research labs, and Miles is our reinforcement-learning post-training framework for large-scale LLM and MoE models. Your work directly advances our mission to democratize AI: every improvement you ship lowers the cost and raises the ceiling of what developers everywhere can build.
As our technical face to a community of expert users and partners, you'll push the performance of SGLang and Miles through the lens of real production workloads. You'll profile and optimize GPU performance, enable new models and hardware, build kernels, deliver day-0 model support, and push the limits of inference and training. Working in close partnership with leading teams across the ecosystem, you'll turn their hardest, most ambiguous problems into concrete wins and clear guidance, and feed those improvements back into our systems and future roadmap.
Key Responsibilities
  • Accelerate AI workloads Profile and optimize GPU performance for real production workloads on current and next-generation hardware, root-causing bottlenecks from kernels to distributed multi-node systems.
  • Go deep in one or two focus areas. The team collectively covers the full stack; each engineer specializes in one or two tracks:
    • Inference performance: engine tuning, benchmarking, long-context and multi-turn optimization, parallelism strategy, production debugging
    • Kernels and model/hardware enablement: custom CUDA/ROCm/Triton kernels, low-precision quantization, day-0 support for new models on new silicon
    • Speculative decoding: draft-model training, acceptance-rate tuning, cross-platform kernel adaptation
    • Training systems: RL post-training with Miles, FP8 training, elasticity, long-rollout and long-context efficiency
  • Partner directly with the ecosystem. Turn ambiguous, high-stakes problems from expert engineers at our key partners into concrete wins, clear technical guidance, and reproducible cookbooks.
  • Enhance SGLang and Miles Feed user-driven improvements back into our open-source systems and roadmap, so every win compounds across the ecosystem.

Qualifications
Minimum Requirements
  • 4+ years of experience in GPU systems, LLM infrastructure, or performance engineering.
  • Strong profiling and debugging skills: able to root-cause performance and correctness issues across the stack.
  • Hands-on GPU programming experience in at least one of CUDA, ROCm, or Triton, and willingness to work across platforms.
  • Strong programming skills in Python plus C++ or CUDA.
  • Comfortable making progress on hard, ambiguous problems with little context to start from, and fast to ramp into unfamiliar systems, codebases, and domains.
  • Ability to translate ambiguous asks into clear technical plans, verified cookbooks, and actionable recommendations, and to communicate credibly with expert engineering audiences.

Preferred (Bonus) Qualifications
  • Deep familiarity with LLM inference internals: distributed serving, parallelism, routing, KV-cache management, scheduling.
  • Experience with low-precision quantization and inference/training (FP8, INT8/INT4; NVFP4 or MXFP4 a strong plus).
  • Experience writing and optimizing custom GPU kernels.
  • Practical familiarity with speculative decoding methods such as Eagle, DFlash, or DSpark.
  • Working knowledge of large-scale distributed training: pre-training, SFT, RL post-training, elasticity, long-context workloads.
  • Experience optimizing across both NVIDIA and AMD platforms.
  • Hands-on experience with SGLang, Miles, vLLM, TensorRT-LLM, Megatron, or comparable frameworks; contributions to open-source AI/ML projects.

About RadixArk
RadixArk is an infrastructure-first company built by engineers who've shipped production AI systems, created SGLang (30K+ GitHub stars, the fastest open LLM serving engine), and developed Miles (our large-scale RL framework). Founded by AI infrastructure veterans from xAI and NVIDIA, we're on a mission to democratize frontier-level AI infrastructure by building world-class open systems for inference and training. Our team has optimized kernels serving billions of tokens daily, designed distributed training systems coordinating 10,000+ GPUs, and contributed to infrastructure that powers leading AI companies and research labs.
Compensation
We offer competitive compensation with equity, comprehensive health benefits, and flexible work arrangements. Compensation is determined by location, level, and experience.
Equal Opportunity
RadixArk is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

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