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High Performance Computing Hpc Jobs in California

Nvidia

Senior CPU Performance Architect

Santa Clara, CA · On-site

$196K/yr

Do you want to help drive the development of CPU technology for architectures used for artificial intelligence (AI) / deep learning (DL), high-performance computing (HPC), cloud service providers ...

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High Performance Computing Hpc information

See California salary details

$32.1K

$67.4K

$110.5K

How much do high performance computing hpc jobs pay per year?

As of Jul 10, 2026, the average yearly pay for high performance computing hpc in California is $67,355.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,300.00 and $81,900.00 per year, depending on experience, location, and employer.

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

AspectHigh Performance Computing (HPC)Data Scientist
Required credentialsDegree in Computer Science, Engineering, or related fields; often certifications in parallel computing or HPC systemsDegree in Data Science, Statistics, Computer Science, or related fields; certifications in data analysis or machine learning
Work environmentSupercomputing centers, research labs, large enterprises with high computational needsTech companies, finance, healthcare, research institutions, often in office or remote settings
Industry usageScientific research, simulations, modeling, large-scale data processingData analysis, predictive modeling, machine learning, business insights

While both roles involve working with large datasets and complex computations, HPC specialists focus on designing and maintaining high-performance computing systems for scientific and engineering tasks. Data scientists analyze data to extract insights and build models. The roles often overlap in data processing but differ in technical focus and environment.

What are some common challenges faced by professionals working in High Performance Computing (HPC) environments?

Professionals in HPC roles often encounter challenges such as optimizing code for parallel processing, managing complex and rapidly evolving hardware architectures, and troubleshooting large-scale distributed systems. Collaborating closely with researchers and domain experts is also essential to ensure that computational resources are used efficiently and effectively. Keeping up with advances in both hardware and software, as well as balancing multiple projects with tight deadlines, are typical aspects of the HPC work environment.

What is High Performance Computing (HPC)?

High Performance Computing (HPC) refers to the use of supercomputers and parallel processing techniques to solve complex computational problems quickly and efficiently. HPC systems combine the power of multiple processors to perform billions or even trillions of calculations per second, making them essential for scientific research, engineering simulations, data analytics, and other demanding tasks. These systems are used in fields such as weather forecasting, molecular modeling, financial modeling, and artificial intelligence. By leveraging HPC, organizations can tackle problems that are too large or complex for standard computers.

What are the key skills and qualifications needed to thrive as a High Performance Computing (HPC) specialist, and why are they important?

To thrive as a High Performance Computing (HPC) specialist, you need a solid background in computer science or engineering, strong programming skills (especially in languages like C, C++, or Python), and expertise in parallel computing and Linux systems. Familiarity with cluster management tools, job schedulers (e.g., SLURM or PBS), and experience with HPC libraries and accelerators such as MPI, OpenMP, and GPU programming are typically required. Excellent problem-solving abilities, teamwork, and effective communication skills help you collaborate with researchers and resolve complex technical challenges. These competencies are vital for optimizing computational workflows, maintaining robust systems, and enabling advanced scientific or industrial research.
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High Performance Computing Hpc jobs near you
Infographic showing various High Performance Computing Hpc job openings in California as of July 2026, with employment types broken down into 2% Internship, 87% Full Time, 2% Part Time, 7% Contract, and 2% Nights. Highlights an 98% In-person, and 2% Hybrid job distribution, with an average salary of $67,355 per year, or $32.4 per hour.
Java Developer - High Performance Computing

Java Developer - High Performance Computing

IT America Inc

San Jose, CA • On-site

$59.75 - $77.25/hr

Contractor

Re-posted 10 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).