GPU Solver Engineer - Multiphysics & Numerical Methods

Nebula; El Segundo (CA); Hybrid; USD 100,000 - 350,000; Full time

Benefits offered by this job

Equity participation

Health, dental, and vision insurance

401(k) with company matching

On-site work with limited WFH

Merit-based rewards

Job summary

Nebula in El Segundo is seeking a GPU and numerical methods engineer to build the linear algebra and GPU compute under a multiphysics solver stack. You will optimize matrix-free operators, Krylov solvers, and preconditioning, using Vulkan, CUDA or HIP to deliver fast, reproducible solves.

You should have a degree in CS or applied mathematics and hands-on GPU kernel experience with sparse linear algebra, mixed precision, and deterministic results.

Qualifications

• Experience with sparse/iterative linear algebra and solver analysis.
• Hands-on GPU compute kernel tuning in Vulkan, CUDA or HIP for numerical workloads.
• Ability to reason about solver pushback and performance on heterogeneous hardware.

Responsibilities

• Develop matrix-free operators for SPD systems across physics families.
• Implement Krylov solvers and preconditioning, incl. multigrid and deflation.
• Ensure convergence with fixed-order reductions and reproducibility across devices.
• Work with mixed precision and verify numerical stability in long runs.
• Profile, optimize memory layout, and perform roofline analysis.
• Collaborate on interactive tiers and distributed execution with remote accelerators.

Skills

Sparse linear algebra

Iterative solvers

Preconditioning & multigrid

GPU kernel performance

Numerical reproducibility

Education

Degree in CS / computational science / applied mathematics

Tools

Vulkan

CUDA

HIP

Job description

Nebula in El Segundo is seeking a GPU and numerical methods engineer to build the linear algebra and GPU compute under a multiphysics solver stack. You will optimize matrix-free operators, Krylov solvers, and preconditioning, using Vulkan, CUDA or HIP to deliver fast, reproducible solves.

You should have a degree in CS or applied mathematics and hands-on GPU kernel experience with sparse linear algebra, mixed precision, and deterministic results.