Differentiable-Physics Scientist for Multiscale Simulations

Periodic; Menlo Park (CA); On-site; USD 250,000 - 350,000; Full time

Job summary

Periodic Labs in Menlo Park, CA is building AI-powered differentiable simulations for continuum-physics problems, including fluid dynamics. The role spans governing equations, solver code, and deep learning to advance surrogate modeling, inverse problems, and optimization.

You will develop differentiable, accelerator-ready solvers, test against experiments and benchmarks, and create datasets to guide LLM-driven automation of physics tasks.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Python

JAX

PyTorch

Julia

C++

Deep learning

PDE solvers

Numerical methods

Fluid dynamics

Education

PhD

Bachelor's degree

Tools

GPU acceleration

Job description

Periodic Labs in Menlo Park, CA is building AI-powered differentiable simulations for continuum-physics problems, including fluid dynamics. The role spans governing equations, solver code, and deep learning to advance surrogate modeling, inverse problems, and optimization.

You will develop differentiable, accelerator-ready solvers, test against experiments and benchmarks, and create datasets to guide LLM-driven automation of physics tasks.