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High Performance Computing Engineer Jobs in California

CoreWeave

GPU Performance Engineer

Sunnyvale, CA · On-site

The GPU Performance Engineer will focus on the design, implementation, and maintenance of cloud ... intelligence and high-performance computing workloads. Founded in 2017, the company is ...

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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 Jul 11, 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

Software Engineer - Nonlinear Solid Mechanics & High-Performance Computing

Vinci AI

Palo Alto, CA • On-site

$190K - $230K/yr

Full-time

Posted 16 days ago

Job description

About Us
At Vinci4d, we are building the next generation of simulation software for thermal, fluid flow, and structural mechanics applications - the kind of tools that change how engineers design products, from the first mesh to the final answer. We are a small, technically deep team that moves fast, ships real software, and takes on hard problems that matter. If you want your work to be foundational to a platform used by engineers worldwide, this is the place.
The Role
We are looking for a software engineer who lives at the intersection of computational solid mechanics, numerical methods, and high-performance computing. You will design, implement, and tune solvers for geometric and material nonlinearity in solid mechanics - think large-deformation, contact, and history-dependent material response - that run at scale on modern hardware. You will write production-quality code, contribute to our CI/CD infrastructure, and collaborate closely with a multi-disciplinary team of physicists, engineers, and software developers.
This is not a "maintain the existing stack" role. You will be building things that don't exist yet, solving problems that require both rigorous mathematical thinking and solid engineering instincts.
What You Will Work On
  • Develop and tune nonlinear solvers for solid mechanics, handling both geometric nonlinearity (large deformation, finite strain) and material nonlinearity (plasticity, viscoelasticity, temperature-dependent and history-dependent constitutive models)
  • Build and optimize the underlying linear algebra: iterative linear solvers and preconditioners for the large sparse systems arising at each Newton iteration
  • Port and optimize these solvers for GPU execution using CUDA, HIP, or equivalent frameworks, with a focus on memory bandwidth, occupancy, and scalability
  • Implement FEM discretizations for structural and thermomechanical field solves, with attention to robustness and convergence under stiff, ill-conditioned, and near-singular conditions
  • Contribute to a robust software engineering foundation: version control discipline, automated testing, CI/CD pipelines, and code review practices
  • Collaborate with domain experts to translate physical models and mathematical formulations into correct, efficient implementations
  • Profile and benchmark solver performance; identify and eliminate bottlenecks

What We Are Looking For
Technical Skills
  • Hands-on experience developing solvers for geometric and material nonlinearity in solid mechanics - large-deformation kinematics, nonlinear constitutive models, and the Newton-type schemes that drive them to convergence
  • Strong foundation in the finite element method (FEM) for solid and structural mechanics
  • Deep familiarity with iterative linear solvers (e.g., Krylov methods) and preconditioning techniques for large, sparse systems, with hands-on experience implementing these inside a nonlinear solver
  • Proven GPU programming experience (CUDA, HIP, SYCL, or similar) with a track record of getting real performance out of hardware
  • Proficiency in C++ and/or Python; comfort working in performance-critical codebases
  • Strong software engineering practices: Git workflows, code review, automated testing (unit, integration, regression), and CI/CD pipelines

Experience
  • 3-6 years of industry or research experience in a relevant field (computational mechanics, scientific computing, computational physics, numerical simulation, or HPC)
  • A portfolio of work - open source contributions, published code, or shipped products - that demonstrates the above

Soft Skills
  • A genuine collaborator: you learn from teammates as readily as you help them
  • Able to communicate technical depth clearly to people from different disciplines - physicists, mechanical engineers, product managers
  • Comfortable with ambiguity and excited by the challenges that come with building something new
  • Self-directed and ownership-oriented: you drive your work to completion without needing to be managed closely

Nice to Have
  • Experience with warpage and residual-stress problems in semiconductor manufacturing (e.g., packaging, die/substrate stacks, thermomechanical deformation)
  • Familiarity with matrix-free methods for nonlinear and linear operator application
  • Experience with geometric multigrid approaches as solvers or preconditioners
  • Background in adaptive mesh refinement (AMR)
  • Familiarity with embedded geometry or immersed boundary methods for solid mechanics
  • Experience applying machine learning to solid mechanics problems (surrogates, constitutive modeling, solver acceleration)
  • Experience with performance profiling tools (Nsight, VTune, Roofline analysis)

Why Vinci4d
  • Work on genuinely hard technical problems with real engineering impact
  • Join a small team where your contributions are visible and your voice is heard
  • Competitive compensation with equity participation
  • Flexible work environment
  • The satisfaction of building something from the ground up - and the opportunity to help define what it becomes

Apply