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Entry Level High Performance Computing Engineer Jobs in Berkeley, CA

Anthropic

Performance Engineer

San Francisco, CA

High performance, large-scale ML systems * GPU/Accelerator programming * ML framework internals * OS internals * Language modeling with transformers Representative projects: * Implement low-latency ...

OpenAI

Software Engineer, Hardware

San Francisco, CA · On-site

$225K - $455K/yr

You will work at the intersection of systems programming, ML infrastructure, and high-performance computing, helping to create both ergonomic developer APIs and highly efficient runtime systems. This ...

Rigetti Computing

Quantum Engineer

Berkeley, CA · On-site

$155K - $200K/yr

As a Quantum Engineer at Rigetti Computing, you will be a key contributor to the development of our ... high-performance practical quantum computing. Rigetti has developed the industry's first multi-chip ...

Rigetti Computing

Quantum Engineer

Berkeley, CA

$155K - $200K/yr

As a Quantum Engineer at Rigetti Computing, you will be a key contributor to the development of our ... high-performance practical quantum computing. Rigetti has developed the industry's first multi-chip ...

Becoming

Applied AI Engineer

San Francisco, CA · On-site

$114K - $157K/yr

Support high-performance computing and GPU infrastructure * Optimize cloud utilization and cost ... Engineering, Mathematics, Physics, or related field * At least 1 year of industry experience

Becoming

Applied AI Engineer

San Francisco, CA · On-site

$114K - $157K/yr

Support high-performance computing and GPU infrastructure * Optimize cloud utilization and cost ... Engineering, Mathematics, Physics, or related field * At least 1 year of industry experience

Becoming

Applied AI Engineer

San Francisco, CA

$114K - $157K/yr

Support high-performance computing and GPU infrastructure * Optimize cloud utilization and cost ... Engineering, Mathematics, Physics, or related field * At least 1 year of industry experience

Bronco

Founding Software Engineer

San Francisco, CA · On-site

$150K - $200K/yr

About the role As a founding software engineer at Bronco AI, you will be building world-class ... Experience with high-performance computing and optimization * Familiarity with cloud computing ...

AMAX

Graphic Designer

Fremont, CA

Catering to industries such as AI, cloud computing, autonomous vehicles, and high-performance computing, AMAX has set benchmarks in innovation, including pioneering liquid-cooled HPC systems for the ...

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Entry Level High Performance Computing Engineer information

See Berkeley, CA salary details

$36.7K

$84.9K

$144.5K

How much do entry level high performance computing engineer jobs pay per year?

As of Jul 12, 2026, the average yearly pay for entry level high performance computing engineer in Berkeley, CA is $84,930.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,100.00 and $96,100.00 per year, depending on experience, location, and employer.

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 job categories do people searching Entry Level High Performance Computing Engineer jobs in Berkeley, CA look for? The top searched job categories for Entry Level High Performance Computing Engineer jobs in Berkeley, CA are:
Entry Level High Performance Computing Engineer jobs near you
Infographic showing various Entry Level High Performance Computing Engineer job openings in Berkeley, CA as of July 2026, with employment types broken down into 4% Locum Tenens, 87% Full Time, 8% Part Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $84,930 per year, or $40.8 per hour.
Performance Engineer

Performance Engineer

Anthropic

San Francisco, CA

Other

Re-posted yesterday

Job description

About the role:
Running machine learning (ML) algorithms at our scale often requires solving novel systems problems. As a Performance Engineer, you'll be responsible for identifying these problems, and then developing systems that optimize the throughput and robustness of our largest distributed systems. Strong candidates here will have a track record of solving large-scale systems problems and will be excited to grow to become an expert in ML also.
You may be a good fit if you:
  • Have significant software engineering or machine learning experience, particularly at supercomputing scale
  • Are results-oriented, with a bias towards flexibility and impact
  • Pick up slack, even if it goes outside your job description
  • Enjoy pair programming (we love to pair!)
  • Want to learn more about machine learning research
  • Care about the societal impacts of your work
Strong candidates may also have experience with: 
  • High performance, large-scale ML systems
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers
Representative projects:
  • Implement low-latency high-throughput sampling for large language models
  • Implement GPU kernels to adapt our models to low-precision inference
  • Write a custom load-balancing algorithm to optimize serving efficiency
  • Build quantitative models of system performance
  • Design and implement a fault-tolerant distributed system running with a complex network topology
  • Debug kernel-level network latency spikes in a containerized environment

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

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