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Internship Performance Engine Tuning Jobs in Santa Clara, CA

Astera Labs

Performance Engineer

San Jose, CA

... tuning of real AI workloads on our fabric to inform product direction. Your data will directly ... We welcome both recent graduates with strong, directly relevant project, research, or internship ...

Astera Labs

Performance Engineer

San Jose, CA · On-site

... tuning of real AI workloads on our fabric to inform product direction. Your data will directly ... We welcome both recent graduates with strong, directly relevant project, research, or internship ...

MOTA

SEO / PPC Expert

Sunnyvale, CA

This is not an internship. Analytics, A/B split testing, experiments, conversion rates should be ... Perform analysis of SEO performance using vendor and e-commerce analytics platform. * Implement SEO ...

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.

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All JobsPerformance Engine Tuning JobsInternship Performance Engine Tuning JobsInternship Performance Engine Tuning Jobs in Santa Clara, CA

Internship Performance Engine Tuning information

See Santa Clara, CA salary details

$10

$20

$27

How much do internship performance engine tuning jobs pay per hour?

As of Aug 6, 2026, the average hourly pay for internship performance engine tuning in Santa Clara, CA is $20.32, according to ZipRecruiter salary data. Most workers in this role earn between $16.92 and $22.60 per hour, depending on experience, location, and employer.

What is the difference between Internship Performance Engine Tuning vs Performance Engine Tuner?

AspectInternship Performance Engine TuningPerformance Engine Tuner
CredentialsTypically pursuing or recent graduate, basic knowledgeRelevant certifications or experience in engine tuning
Work EnvironmentTraining setting, supervised projectsProfessional workshop or automotive shop
Industry UsageEntry-level, learning phaseFull-time, specialized role in automotive industry
Search IntentLearning, entry-level opportunitiesProfessional tuning and performance optimization

Internship Performance Engine Tuning is an entry-level position focused on learning and gaining experience under supervision, while Performance Engine Tuner is a professional role requiring experience and certifications to optimize engine performance in a commercial setting.

What kind of projects or tasks can I expect to work on during an internship in performance engine tuning?

As an intern in Performance Engine Tuning, you’ll typically assist with data collection and analysis from engine tests, help calibrate engine control parameters, and support the team in optimizing engine performance for power, efficiency, or emissions targets. You may also contribute to diagnostic troubleshooting and participate in simulation or dynamometer testing. Collaboration with senior engineers and cross-functional teams is common, offering valuable exposure to both technical and team-based problem-solving.

What is an internship performance engine tuning?

Internship Performance Engine Tuning positions are internship roles focused on optimizing and improving engine performance, typically within automotive or motorsports industries. These internships give students or recent graduates hands-on experience with engine calibration, diagnostics, data analysis, and testing. Interns work with experienced engineers to fine-tune engine parameters for increased power, efficiency, and reliability. It's a great way to gain technical skills and industry knowledge, especially for those interested in automotive engineering or high-performance vehicles.

What are the key skills and qualifications needed to thrive as an internship performance engine tuning specialist, and why are they important?

To excel in an Internship Performance Engine Tuning role, a solid understanding of automotive engineering principles, engine mechanics, and coursework in mechanical or automotive engineering are essential. Familiarity with engine management software, diagnostic tools, and data logging systems—plus basic programming or calibration tool proficiency—is highly valued. Strong analytical thinking, attention to detail, and effective communication skills help interns collaborate with teams and troubleshoot complex issues. These abilities are critical for optimizing engine performance while ensuring reliability, safety, and regulatory compliance in real-world automotive applications.
What cities near Santa Clara, CA are hiring for Internship Performance Engine Tuning jobs? Cities near Santa Clara, CA with the most Internship Performance Engine Tuning job openings:
Internship Performance Engine Tuning jobs near you
Infographic showing various Internship Performance Engine Tuning job openings in Santa Clara, CA as of June 2026, with employment types broken down into 98% Full Time, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $42,274 per year, or $20.3 per hour.

Member of Technical Staff - Developer Technology

RadixArk

Palo Alto, CA • On-site

Full-time

Posted 14 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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