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Entry Level High Performance Computing Engineer Jobs in California

RadixArk

Performance Engineer

Palo Alto, CA · On-site

... high-performance computing • Familiarity with profiling tools, performance debugging, tracing ... both engineers and customers Preferred : • Experience with CUDA, Triton, Pallas, ROCm, XLA, or ...

RadixArk

Performance Engineer

Palo Alto, CA · On-site

... high-performance computing • Familiarity with profiling tools, performance debugging, tracing ... both engineers and customers Preferred : • Experience with CUDA, Triton, Pallas, ROCm, XLA, or ...

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Entry Level High Performance Computing Engineer information

What is an Entry Level High Performance Computing Engineer?

An Entry Level High Performance Computing (HPC) Engineer is a professional who assists in designing, building, and maintaining high-speed computing systems used for complex computations and large-scale data analysis. They typically work with supercomputers or computer clusters in fields like scientific research, finance, or engineering. Responsibilities often include configuring hardware, optimizing software, and troubleshooting system issues, usually under the guidance of more experienced engineers. Entry-level engineers may also help monitor system performance and support users in running high-performance applications.

What are some common challenges faced by entry level High Performance Computing (HPC) engineers, and how can new hires successfully navigate them?

Entry level HPC engineers often encounter challenges such as working with complex parallel computing architectures, optimizing code for performance, and troubleshooting across large-scale, distributed systems. New hires may also need to quickly learn job-specific tools and adapt to rapidly evolving hardware and software environments. To navigate these challenges, it’s important to proactively seek mentorship, participate in team code reviews, and continuously build your skills through hands-on experience and training opportunities. Open communication and collaboration with experienced team members also play a key role in overcoming technical hurdles and growing within the HPC field.

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

AspectEntry Level High Performance Computing EngineerEntry Level Data Scientist
Required CredentialsBachelor's in Computer Science, Engineering, or related field; knowledge of parallel computingBachelor's in Data Science, Statistics, or related; programming skills in Python/R
Work EnvironmentResearch labs, tech companies, supercomputing centersBusiness, tech firms, research institutions
Industry UsageHigh-performance computing, scientific research, simulationsData analysis, machine learning, predictive modeling

Entry Level High Performance Computing Engineers focus on developing and optimizing computational systems for scientific and technical applications, while Entry Level Data Scientists analyze data to extract insights. Both roles require programming skills and a strong technical background, but they serve different industry needs and environments.

What are the key skills and qualifications needed to thrive as an Entry Level High Performance Computing (HPC) Engineer, and why are they important?

To thrive as an Entry Level High Performance Computing Engineer, you typically need a solid background in computer science or engineering, familiarity with parallel computing concepts, and proficiency in programming languages like C/C++ or Python. Experience with Linux environments, HPC cluster management tools, and knowledge of batch schedulers or MPI/OpenMP are often required. Strong problem-solving abilities, teamwork, and effective communication help you excel in collaborating with researchers and technical teams. These skills ensure efficient support and optimization of complex computing systems critical for scientific and technical advancements.
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:
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What cities in California are hiring for Entry Level High Performance Computing Engineer jobs? Cities in California with the most Entry Level High Performance Computing Engineer job openings:
Entry Level High Performance Computing Engineer jobs near you
Infographic showing various Entry Level High Performance Computing Engineer job openings in California as of July 2026, with employment types broken down into 50% Full Time, and 50% Contract. Highlights an 100% In-person job distribution.

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).