About the RoleYou should be equally comfortable with governing equations, solver code, and deep learning. We are open to expertise in any area of continuum-physics, with at least some experience in fluid dynamics. You will work on building simulation capabilities in challenging, data-limited domains requiring a mix of physics-based and empirical approaches.
What You'll Do- Build and extend differentiable solvers for continuum simulation (including but not limited to fluid dynamics), especially multi-scale and multi-physics problems.
- Implement numerical methods from equations and papers, and diagnose 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 modern accelerators with JAX or PyTorch to make simulations scalable and trainable.
- Validate models against experiments, trusted benchmarks, or high-fidelity simulations.
- Create datasets and evaluations to guide the development of LLMs to accelerate and automate these tasks.
You Will Thrive Here If You Have- A 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 domain, with breadth across domains or a demonstrated ability to learn new physics quickly.
- Meaningful experience building, training, and evaluating deep-learning models for physical systems.
- Strong Python and software-engineering skills, especially JAX, PyTorch, Julia, or C++.
- Experience applying simulation to realistic scientific or engineering problems, not only clean academic benchmarks.
- A startup mentality: ownership, good judgment under uncertainty, and enthusiasm for building from scratch.
Strong Candidates May Also Have- Experience with fluid dynamics plus another continuum domain, or with multiphysics and 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.
Mechanics- Minimum education: Bachelor's degree or similar experience
- Location: Menlo Park, CA (Soon: San Francisco, too)
- Compensation: $250,000-350,000 + equity
- Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.
