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Internship 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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Internship High Performance Computing Engineer information

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

To thrive as an Internship High Performance Computing Engineer, you need a solid background in computer science fundamentals, programming (especially in C/C++ or Python), and a familiarity with parallel computing concepts, often supported by coursework or relevant project experience. Experience with Linux environments, HPC clusters, and distributed computing frameworks, as well as tools like MPI, OpenMP, or Slurm, is commonly required. Strong problem-solving skills, attention to detail, and the ability to collaborate effectively within technical teams help interns stand out. These skills ensure you can efficiently support computational research, resolve technical challenges, and contribute meaningfully to HPC projects.

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

AspectInternship High Performance Computing EngineerInternship Data Scientist
Required SkillsProgramming (C++, Python), parallel computing, HPC systemsStatistics, machine learning, data analysis, Python/R
Work EnvironmentResearch labs, tech companies, academia with focus on HPC systemsTech firms, finance, healthcare, research institutions
Industry UsageHigh-performance computing projects, scientific simulationsData analysis, predictive modeling, business insights

Internship High Performance Computing Engineers focus on developing and optimizing computational systems for large-scale scientific and engineering problems, requiring skills in parallel programming and HPC environments. In contrast, Internship Data Scientists analyze data to extract insights, using statistical and machine learning techniques. Both roles are valuable in tech and research sectors but differ in technical focus and daily tasks.

What is an Internship High Performance Computing Engineer?

An Internship High Performance Computing (HPC) Engineer is a student or early-career professional who works with advanced computing systems designed for processing large data sets and complex calculations at high speeds. During the internship, they assist in developing, optimizing, and maintaining HPC infrastructure, software, or applications used in scientific research, engineering, or data analysis. The role often involves learning about parallel computing, cluster management, and performance tuning, while gaining hands-on experience with cutting-edge technologies. Interns work under the supervision of experienced HPC engineers, contributing to projects that advance computational capabilities in various fields.

What types of projects can I expect to work on as an Internship High Performance Computing Engineer?

As an Internship High Performance Computing (HPC) Engineer, you will typically contribute to projects involving optimization of scientific applications, performance analysis, and cluster management. Interns often assist with benchmarking software, troubleshooting issues in parallel computing environments, and supporting researchers with technical solutions. You'll likely collaborate closely with senior HPC engineers, system administrators, and academic researchers to ensure efficient use of computing resources. This hands-on experience provides valuable insight into real-world challenges faced in HPC environments and helps build a strong foundation for future roles in the field.
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Internship 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