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High Performance Computing Engineer Jobs in California

Advanced Micro Devices

Thermal Engineer

San Jose, CA · On-site

$140 - $190/hr

AMD is powering the next generation of supercomputing, high-performance computing, cloud, and AI ... THE PERSON: You are passionate about thermal engineering and advanced electronics cooling ...

Socket.dev

Thermal Engineer

San Jose, CA · On-site

$120 - $150/hr

AMD is powering the next generation of supercomputing, high-performance computing, cloud, and AI ... THE PERSON: You are passionate about thermal engineering and advanced electronics cooling ...

NVIDIA

Senior Fortran Compiler Engineer

Santa Clara, CA · On-site

$122K - $168K/yr

NVIDIA's HPC compiler group is seeking a Fortran compiler developer to contribute to the ... high-performance computing, while implementing and improving features in LLVM Flang, OpenACC, and ...

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High Performance Computing Engineer information

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$10

$59

$96

How much do high performance computing engineer jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for high performance computing engineer in California is $59.32, according to ZipRecruiter salary data. Most workers in this role earn between $48.65 and $67.12 per hour, depending on experience, location, and employer.

What is a high performance computing engineer?

A High Performance Computing (HPC) Engineer is a specialist who designs, builds, and maintains advanced computing systems that deliver exceptional processing power for complex computational tasks. These professionals optimize hardware and software environments to support scientific research, large-scale simulations, and data-intensive applications. They work with supercomputers, clusters, and cloud HPC resources, ensuring high efficiency, scalability, and reliability. HPC Engineers also support researchers and organizations in maximizing the performance of their computing infrastructure.

What are the key skills and qualifications needed to thrive as a high performance computing engineer?

To thrive as a High Performance Computing (HPC) Engineer, you need a strong background in computer science, parallel programming, and distributed systems, typically supported by a relevant degree. Familiarity with HPC clusters, Linux/Unix environments, programming languages like C/C++ or Python, and tools such as MPI, OpenMP, and job schedulers is essential. Analytical thinking, problem-solving, and effective teamwork are crucial soft skills for optimizing system performance and collaborating with researchers or end-users. These abilities ensure efficient computational solutions, maximize resource utilization, and drive innovation in data-intensive scientific or engineering projects.

What are some common challenges high performance computing engineers face when optimizing system performance?

High Performance Computing Engineers often encounter challenges such as balancing resource allocation, managing workload distribution, and minimizing system bottlenecks. They must ensure that hardware and software components interact efficiently, which can require deep knowledge of parallel computing, networking, and storage systems. Additionally, staying up-to-date with rapidly evolving technologies and troubleshooting complex performance issues are integral parts of the role. Collaborating closely with researchers and IT teams is essential to tailor solutions that meet specific computational needs.

What is the difference between High Performance Computing Engineer vs Data Scientist?

AspectHigh Performance Computing EngineerData Scientist
Required CredentialsBachelor's or master's in computer science, engineering, or related fields; knowledge of parallel computingBachelor's or master's in data science, statistics, or related fields; programming skills in Python, R
Work EnvironmentResearch labs, tech companies, supercomputing centersBusiness, tech firms, research institutions
Industry UsageSupercomputing, scientific research, simulationsData analysis, machine learning, predictive modeling

High Performance Computing Engineers focus on developing and optimizing large-scale computing systems for scientific and technical applications, while Data Scientists analyze data to extract insights. Both roles require programming skills and work in tech-driven environments, but their core objectives differ: system performance versus data analysis.

What are the most commonly searched types of High Performance Computing Engineer jobs in California?

The most popular types of High Performance Computing Engineer jobs in California are:

What are popular job titles related to High Performance Computing Engineer jobs in California?

For High Performance Computing Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching High Performance Computing Engineer jobs in California look for?

The top searched job categories for High Performance Computing Engineer jobs in California are:

High Performance Computing Engineer jobs near you
Infographic showing various High Performance Computing Engineer job openings in California as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 13% Part Time, 2% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $123,381 per year, or $59.3 per hour.

Research Computing Cloud Engineer

Stanford University

Stanford, CA • On-site

$65.50 - $87.75/hr

Full-time

Re-posted 17 days ago

Stanford University rating

7.9

Company rating: 7.9 out of 10

Based on 25 frontline employees who took The Breakroom Quiz

209th of 621 rated colleges and universities

Job description

Job Summary:
Stanford University is seeking a highly skilled Cloud Engineer to help build the next generation of its research computing environment. This role involves designing and implementing scalable, secure cloud architectures for various projects, including high-performance computing and AI/ML workloads.
Responsibilities:
• Architect cloud-native and hybrid solutions for compute intensive workloads, such as AI training and genomics.
• Develop automated pipelines to "burst" workloads from on-premises HPC clusters to the cloud (AWS/GCP/Azure).
• In collaboration with other campus, and cloud vendor based, experts provide guidance on the use of cloud technologies to support research - including cloud architecture, cost optimization, and performance tuning.
Qualifications:
Required:
• Bachelor's degree and eight years of relevant experience or a combination of education and relevant experience.
• Advanced experience with major cloud platforms: GCP, AWS or Azure
• Hands-on experience with HPC job schedulers (e.g., Slurm) and/or GPU computing (CUDA, NVIDIA software stacks).
• Proficiency in Infrastructure as Code (Terraform, Ansible) and containerization (Docker, Kubernetes).
• Experience with orchestration of data movement between on premise and cloud environments.
• Understanding of data privacy frameworks (HIPAA, CMMC) within a research context.
Preferred:
• Degree in Computer Science, Engineering, or equivalent professional experience in a large-scale research computing environment or similar is preferred.
• Experience with cloud-bursting and/or integration of on-premise systems with cloud environments is a plus.
• Experience with Globus a plus.
Company:
Stanford University is a teaching and research university that focuses on graduate programs in law, medicine, education, and business. Founded in 1885, the company is headquartered in Stanford, USA, with a team of 10001+ employees. The company is currently Late Stage.

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