Computational Scientist (AI)

Location: Menlo Park, California; on-site. The role is expected to also be available in San Francisco soon.

Compensation: $250,000–$350,000 annually plus equity

Company

Build AI systems that simulate physical science, verify predictions, and learn from the full scientific method.

What you will do

• Build and extend differentiable solvers for continuum simulations, including fluid dynamics and multi-scale, multi-physics problems.
• Implement numerical methods from equations and research papers while diagnosing convergence, stability, and modeling failures.
• Combine simulation with deep learning for surrogate modeling, learned physics, inverse problems, parameter estimation, and optimization.
• Use automatic differentiation and accelerators with JAX or PyTorch to make simulations scalable and trainable.
• Validate models against experiments, trusted benchmarks, and high-fidelity simulations.
• Create datasets and evaluations to help LLMs accelerate and automate scientific simulation tasks.

Requirements

• PhD or equivalent research experience in applied mathematics, computational science, physics, engineering, computer science, or a related field.
• Code-level experience building or substantially modifying PDE solvers, numerical methods, or differentiable simulations.
• Deep expertise in at least one continuum-physics domain, with at least some experience in fluid dynamics.
• Meaningful experience building, training, and evaluating deep-learning models for physical systems.
• Strong Python and software-engineering skills, especially with JAX, PyTorch, Julia, or C++.
• Experience applying simulation to realistic scientific or engineering problems beyond clean academic benchmarks.

Nice to have

• Experience with fluid dynamics plus another continuum domain, multiphysics, or multiscale modeling.
• Expertise in adjoint methods, implicit differentiation, differentiable programming, or scientific optimization.
• Experience accelerating scientific software on GPUs or TPUs.
• Contributions to scientific open-source software used by others.
• Experience connecting simulation to experiments, engineering decisions, semiconductors, or autonomous workflows.

Culture & Benefits

• Startup environment emphasizing ownership and good judgment under uncertainty.
• Opportunity to build scientific simulation capabilities from scratch in data-limited domains.
• Visa sponsorship is available, with assistance throughout the process.
• Equity is included in the compensation package.