About the Role Periodic Labs is building AI systems that can simulate physical science, verify predictions, and train on the full scientific method. We are looking for a Computational Scientist to build differentiable, accelerator-ready simulations for industrially relevant continuum-physics problems. You 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 • Compensation: $250,000-350,000 + equity • Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.