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High Performance Computing Jobs in Santa Clara, CA

RadixArk

Performance Engineer

Palo Alto, CA · On-site

... or high-performance computing • Familiarity with profiling tools, performance debugging, tracing, and benchmark methodology • Comfort working with Python and C++ • Understanding of LLM ...

RadixArk

Performance Engineer

Palo Alto, CA · On-site

... high-performance computing • Familiarity with profiling tools, performance debugging, tracing, and benchmark methodology • Comfort working with Python and C++ • Ability to debug messy real ...

NVIDIA

Senior Fortran Compiler Engineer

Santa Clara, CA · On-site

$122K - $168K/yr

The role involves solving complex problems at the intersection of compilers and high-performance computing, while implementing and improving features in LLVM Flang, OpenACC, and OpenMP.

Conductor

Senior Performance Engineer

San Jose, CA · On-site

$138 - $206/hr

S. with 5+ years of experience in performance engineering, AI systems, distributed systems, high-performance computing, or a related area. MS in Computer/Electrical Engineering or Computer Science ...

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High Performance Computing information

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$47K

$116.9K

$180.2K

How much do high performance computing jobs pay per year?

As of Sep 5, 2026, the average yearly pay for high performance computing in Santa Clara, CA is $116,851.00, according to ZipRecruiter salary data. Most workers in this role earn between $76,900.00 and $147,900.00 per year, depending on experience, location, and employer.

What is high performance computing?

A High Performance Computing (HPC) job involves designing, managing, and optimizing advanced computing systems used for complex calculations, simulations, and data processing. Professionals in this field work with supercomputers, parallel computing frameworks, and high-speed networks to enhance computational efficiency. HPC specialists are commonly employed in scientific research, engineering, finance, and artificial intelligence to solve large-scale problems. Responsibilities often include developing algorithms, maintaining HPC clusters, and improving system performance.

What are the typical responsibilities of someone working in high performance computing?

Professionals in High Performance Computing (HPC) are often responsible for designing, implementing, and maintaining powerful computing clusters tailored for processing large data sets or running complex simulations. Daily tasks may include optimizing code and workflows for parallel environments, troubleshooting hardware and software issues, and supporting researchers or engineers in using HPC resources efficiently. Collaboration is common, as HPC specialists work closely with IT staff, domain scientists, and software developers to ensure systems meet project and organizational goals. This role provides a challenging and dynamic work environment, offering opportunities to continually learn about emerging technologies and methodologies in computational science.

What are the key skills and qualifications needed to thrive in high performance computing, and why are they important?

To thrive in High Performance Computing, you need expertise in parallel computing, computer architecture, and programming languages such as C/C++ or Fortran, often backed by a relevant degree in computer science or engineering. Familiarity with HPC cluster management, job scheduling systems (e.g., SLURM), and experience with accelerators like GPUs or cloud platforms is crucial; certifications in Linux administration or HPC technologies are advantageous. Strong problem-solving skills, attention to detail, and effective communication abilities help professionals excel in complex, collaborative environments. These qualifications enable the efficient design, deployment, and maintenance of advanced computing infrastructure to support scientific and engineering applications.

Is high performance computing still relevant?

High Performance Computing (HPC) remains highly relevant as it enables complex data processing, scientific simulations, and large-scale analytics across industries such as research, finance, and technology. HPC specialists with skills in parallel programming, cluster management, and relevant tools like MPI or CUDA are in demand to support advancements in AI, climate modeling, and big data analysis.

What are examples of high performance computing?

High Performance Computing (HPC) involves using powerful supercomputers and parallel processing techniques to solve complex computational problems. Examples include climate modeling, molecular simulations, financial risk analysis, and large-scale data processing in scientific research. HPC jobs often require knowledge of programming languages like C++ or Fortran, and familiarity with cluster management and parallel computing frameworks such as MPI or OpenMP.

What are the most commonly searched types of High Performance Computing jobs in Santa Clara, CA?

The most popular types of High Performance Computing jobs in Santa Clara, CA are:

What are popular job titles related to High Performance Computing jobs in Santa Clara, CA?

For High Performance Computing jobs in Santa Clara, CA, the most frequently searched job titles are:

What job categories do people searching High Performance Computing jobs in Santa Clara, CA look for?

The top searched job categories for High Performance Computing jobs in Santa Clara, CA are:

What cities near Santa Clara, CA are hiring for High Performance Computing jobs?

Cities near Santa Clara, CA with the most High Performance Computing job openings:

High Performance Computing jobs near you
Infographic showing various High Performance Computing job openings in Santa Clara, CA as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 16% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 1% Hybrid, and 13% Remote job distribution, with an average salary of $116,889 per year, or $56.2 per hour.

Performance Engineer

RadixArk

Palo Alto, CA • On-site

Full-time

Re-posted 20 days ago

Job description

Job Summary:
RadixArk is an infrastructure-first company focused on building world-class open systems for AI inference and training. They are seeking a Performance Engineer to enhance the performance of their AI systems across various environments, ensuring optimal usability, affordability, and reliability in production.
Responsibilities:
• Analyze and improve performance across SGLang, Miles, and RadixArk production deployments
• Benchmark LLM inference and training workloads across GPUs, TPUs, and cloud environments
• Optimize latency, throughput, memory usage, batching, scheduling, routing, and GPU utilization
• Investigate performance regressions in real customer environments
• Work closely with kernel, runtime, distributed systems, and product engineers
• Build internal tooling for profiling, tracing, benchmarking, and regression detection
• Translate customer workload characteristics into concrete performance tuning strategies
• Help define performance metrics that matter commercially, including cost-per-token and serving efficiency
• Partner with customers and cloud partners on deep technical evaluations
• Contribute performance insights back to open-source SGLang and Miles
Qualifications:
Required:
• Strong systems engineering background, especially in performance-critical software
• Experience with GPU systems, distributed systems, inference serving, ML runtimes, or high-performance computing
• Familiarity with profiling tools, performance debugging, tracing, and benchmark methodology
• Comfort working with Python and C++
• Understanding of LLM inference concepts such as batching, KV cache, prefill/decode, speculative decoding, MoE, long context, and P99 latency
• Ability to debug messy real-world performance issues across software, hardware, and infrastructure layers
• Strong communication skills — you should be able to explain performance tradeoffs to both engineers and customers
Preferred:
• Experience with CUDA, Triton, Pallas, ROCm, XLA, or kernel-level optimization is a strong plus
• Prior experience with production AI infrastructure, cloud GPU environments, or open-source ML systems is a plus
Company:
RadixArk focuses on developing infrastructure for AI inference and training systems. Founded in 2025, the company is headquartered in San Francisco, USA, with a team of 11-50 employees. The company is currently Early Stage.

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