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High Performance Computing Engineer Jobs in California

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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 1, 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 are the key skills and qualifications needed to thrive as a High Performance Computing Engineer, and why are they important?

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 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 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 July 2026, with employment types broken down into 2% Locum Tenens, 78% Full Time, 17% Part Time, and 3% Contract. Highlights an 79% Physical, 3% Hybrid, and 18% Remote job distribution, with an average salary of $123,381 per year, or $59.3 per hour.

Java Developer - High Performance Computing

IT America Inc

San Jose, CA • On-site

$59.75 - $77.25/hr

Contractor

Re-posted 2 days ago

Job description

Position: Java Developer – High Performance Computing

Location: San Jose, CA (Hybrid)

Duration: W2 Contract

Job Description:

We are looking for a highly skilled Java Developer with strong experience in High Performance Computing (HPC) and Spring Boot to join our team in a hybrid role based in San Jose, CA. The ideal candidate will be passionate about building high-throughput, low-latency systems and developing robust backend services using modern Java frameworks.

Key Responsibilities:

  • Design, develop, and maintain scalable Java applications with a focus on performance and efficiency.
  • Build microservices and backend systems using Spring Boot.
  • Collaborate with cross-functional teams to solve complex computational problems.
  • Optimize applications for speed and scalability across distributed systems.
  • Conduct unit testing and participate in peer code reviews.

Required Skills:

  • Strong proficiency in Java with hands-on experience in real-time or high-performance systems.
  • Solid experience in High Performance Computing (HPC) environments or applications.
  • Expertise in Spring Boot for developing RESTful APIs and microservices.
  • Good understanding of concurrency, multithreading, and performance tuning.
  • Ability to write clean, efficient, and maintainable code.

Preferred Qualifications:

  • Experience with cloud environments (e.g., AWS, Azure, or GCP).
  • Familiarity with CI/CD pipelines and DevOps tools.
  • Exposure to containerization (Docker) and orchestration tools (Kubernetes).