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High Performance Computing Jobs in Berkeley, CA (NOW HIRING)

PacBio

Staff Engineer, Infrastructure Platforms

Menlo Park, CA · Hybrid

$126K - $166K/yr

Support enterprise storage, virtualization, networking, and High Performance Computing (HPC) platforms supporting research and enterprise workloads. * Serve as the senior technical escalation point ...

PacBio

Staff Engineer, Infrastructure Platforms

Menlo Park, CA · On-site

$126K - $166K/yr

Support enterprise storage, virtualization, networking, and High Performance Computing (HPC) platforms supporting research and enterprise workloads. * Serve as the senior technical escalation point ...

AMAX

Junior System Engineer

Fremont, CA

$80K - $95K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

This role will focus on the design, deployment, validation, and troubleshooting of high-performance computing systems and server platforms. The ideal candidate has strong experience working with ...

Introl

Data Center Technician

San Francisco, CA · On-site

$30 - $36/hr

  • Medical

  • Dental

  • Vision

  • Retirement

We set the benchmark for excellence with our top-tier solutions in high-performance computing deployments, supporting the advancement of AI computing with precision, speed, and minimal downtime. Our ...

AMAX

Shipping Lead

Fremont, CA · On-site

$19.75 - $24.75/hr

  • Medical

  • Dental

  • Vision

  • Retirement

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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High Performance Computing information

See Berkeley, CA salary details

$49K

$121.9K

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How much do high performance computing jobs pay per year?

As of Aug 14, 2026, the average yearly pay for high performance computing in Berkeley, CA is $121,866.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,200.00 and $154,300.00 per year, depending on experience, location, and employer.

What are the typical responsibilities of someone working in high performance computing?

Professionals in High Performance Computing (HPC) are often responsible for designing, implementing, and maintaining powerful computing clusters tailored for processing large data sets or running complex simulations. Daily tasks may include optimizing code and workflows for parallel environments, troubleshooting hardware and software issues, and supporting researchers or engineers in using HPC resources efficiently. Collaboration is common, as HPC specialists work closely with IT staff, domain scientists, and software developers to ensure systems meet project and organizational goals. This role provides a challenging and dynamic work environment, offering opportunities to continually learn about emerging technologies and methodologies in computational science.

Is high performance computing still relevant?

High Performance Computing (HPC) remains highly relevant as it enables complex data processing, scientific simulations, and large-scale computations across industries such as research, finance, and engineering. HPC specialists are in demand for skills in parallel programming, cluster management, and optimizing computational workflows to handle growing data and performance needs.

What are the key skills and qualifications needed to thrive in high performance computing, and why are they important?

To thrive in High Performance Computing, you need expertise in parallel computing, computer architecture, and programming languages such as C/C++ or Fortran, often backed by a relevant degree in computer science or engineering. Familiarity with HPC cluster management, job scheduling systems (e.g., SLURM), and experience with accelerators like GPUs or cloud platforms is crucial; certifications in Linux administration or HPC technologies are advantageous. Strong problem-solving skills, attention to detail, and effective communication abilities help professionals excel in complex, collaborative environments. These qualifications enable the efficient design, deployment, and maintenance of advanced computing infrastructure to support scientific and engineering applications.

What is high performance computing?

A High Performance Computing (HPC) job involves designing, managing, and optimizing advanced computing systems used for complex calculations, simulations, and data processing. Professionals in this field work with supercomputers, parallel computing frameworks, and high-speed networks to enhance computational efficiency. HPC specialists are commonly employed in scientific research, engineering, finance, and artificial intelligence to solve large-scale problems. Responsibilities often include developing algorithms, maintaining HPC clusters, and improving system performance.

What are examples of high performance computing?

High Performance Computing (HPC) involves using powerful supercomputers and parallel processing techniques to solve complex computational problems. Examples include climate modeling, molecular simulations, large-scale data analysis, and scientific research that require high processing speeds and large memory capacities. HPC jobs often involve working with specialized hardware, software, and programming skills such as MPI or CUDA.

What cities near Berkeley, CA are hiring for High Performance Computing jobs?

Cities near Berkeley, CA with the most High Performance Computing job openings:

High Performance Computing jobs near you
Infographic showing various High Performance Computing job openings in Berkeley, CA as of July 2026, with employment types broken down into 1% As Needed, 63% Full Time, 32% Part Time, and 4% Contract. Highlights an 87% Physical, 1% Hybrid, and 12% Remote job distribution, with an average salary of $121,866 per year, or $58.6 per hour.

Software Engineer, High Performance Computing

Eventual Computing

San Francisco, CA • On-site

$150K - $250K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 13 days ago

Job description

About Eventual
Every breakthrough Physical AI system - humanoid robots, autonomous vehicles, video generation models - is trained on petabytes of video, lidar, radar, and sensor data. But today's data platforms (Databricks, Snowflake) were built for spreadsheet-like analytics, not the multimodal corpora that power AI. Robotics and video-AI teams now lose 20-40% of their training time to dataloading alone. GPU bandwidth has grown 2-3× per generation. Storage and pipelines haven't. The gap widens every year.
Eventual was founded in 2022 to close it. Our open-source engine, Daft, is the distributed data engine purpose-built for multimodal AI - already running 2 PB/day at Amazon, 60-100 PB at another FAANG company, and in production at Mobileye, TogetherAI, and CloudKitchens. We are building a video-native index on top of our engine for Physical AI that streams curated datasets to GPUs at line rate. Saturates B200s today. Aimed at NVL72 and Vera Rubin tomorrow.
We're building this in partnership with the top PhysicalAI labs and public AI infrastructure companies today. We have raised $30M from Felicis, CRV, Microsoft M12, Citi, Essence, Y Combinator, Caffeinated Capital, Array.vc, and angels from the co-founders of Databricks and Perplexity. We've assembled a world-class team from AWS, Render, Pinecone and Tesla. We have spent our careers powering the last generation of PhysicalAI in self-driving, and are excited to now do this for the next.
Join our small (but powerful!) team working together 4 days/week in our SF Mission district office.
Your Role
As a Systems Engineer on the Dataloading team, you'll build the layer that turns multi-petabyte video corpora into dict[str, Tensor] already on the GPU at line rate. We work with the top labs training Physical AI on the newest generation hardware - H100, B200, GB200, NVL72, with Vera Rubin on the horizon - on billions of dollars worth of compute, in collaboration with partners that are the largest public AI companies on Earth. Our job is to keep those GPUs fed: rank-aware sampling, NVMe caching, video and sensor co-loading, random access into clips, decode pipelining. Streaming alone can already saturate a B200; the hard part is enabling the complex sampling patterns researchers actually need without giving up a single percentage point of MFU.
This is a systems engineering role for someone who feels physical pain when a system is slow. You won't need GPU experience on day one - we'll uplevel you on NVL72, CUDA, and SLURM. We will need you to bring real expertise on what happens between NVMe, network, memory, and CPU, and a deep instinct for where bytes go.
Key Responsibilities
  • Design and build the video-native dataloader: rank-aware, NVMe-cached, random-access into clips, returns tensors directly to the GPU.
  • Profile and optimize the full data path from object store → NVMe → page cache → host RAM → device RAM. Eliminate every avoidable copy and stall.
  • Saturate the latest hardware (B200, GB200, NVL72) on real customer training jobs. Push toward Vera Rubin bandwidth requirements.
  • Own performance benchmarks against customer baselines (custom DataLoaders, DALI, decord, LeRobot) and against our own historical numbers - regressions get caught at PR time.
  • Partner with researchers at our partner labs to land the loader in their training stack and measure MFU end-to-end.
  • Work cross-team with Storage Infrastructure on the index/format boundary and with Visual Understanding on the model-output ingestion path.

What we look for
  • Obsession with systems-level performance. You can recite Jeff Dean's "numbers every programmer should know" in your sleep. You eat flamegraphs for breakfast.
  • Strong opinions on io_uring - love it or hate it, you've earned the opinion.
  • Live and breathe Rust, C++, or C. You reach for them when it matters and you know why.
  • Strong familiarity with operating systems - page cache, scheduling, syscalls, NUMA, memory hierarchies.
  • A sense for where bytes actually go: NVMe vs. memory vs. network vs. PCIe vs. NVLink, and the throughput and latency budgets of each.

Nice to have
  • Experience working with GPUs is a plus, but you don't need it on day one.
  • Experience working with SLURM, Kubernetes for GPU workloads, or other HPC schedulers.
  • Hands-on CUDA experience.
  • Deep expertise on memory and caching subsystems - page cache tuning, hugepages, NUMA pinning, GPU-Direct Storage.
  • Worked on video decode pipelines (PyAV, decord, NVDEC) or PyTorch DataLoader internals.
  • Contributed to open-source systems projects in Rust/C++.

Perks & Benefits
  • In-person, tight-knit team - 4 days/week in our SF Mission office.
  • Competitive comp and meaningful startup equity.
  • Catered lunches and dinners for SF employees.
  • Commuter benefit.
  • Team-building events and poker nights.
  • Health, vision, and dental coverage.
  • Flexible PTO.
  • Latest Apple equipment.
  • 401(k) plan with match.

If slow systems evoke emotional pain for you and you want to spend the next few years making the most expensive GPU clusters on the planet earn their keep, we'd love to talk.

Apply