---
title: "Computational Scientist, Differentiable Physics"
company: "Periodic Labs"
company_url: "https://www.remjobs.works/companies/periodic-labs"
url: "https://www.remjobs.works/job/periodic-labs-computational-scientist-differentiable-physics-79c941b4-6488-46b4-8568-7e336c43aadb"
apply_url: "https://jobs.ashbyhq.com/periodic-labs/ccdb34b1-b67c-4dbf-8acb-fe786f2ab38b"
workplace: onsite
location: "Menlo Park, CA"
employment_type: full-time
seniority: mid
role: other
region: united-states
skills: ["cpp", "llm", "python", "pytorch"]
date_posted: 2026-09-04T17:25:00.555Z
first_seen_by_remjobs: 2026-09-15T16:21:44.652Z
---

# Computational Scientist, Differentiable Physics

**Periodic Labs** · Menlo Park, CA

Apply: https://jobs.ashbyhq.com/periodic-labs/ccdb34b1-b67c-4dbf-8acb-fe786f2ab38b

## About the role

#### 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 (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.

---

Source: Periodic Labs's own career page, read by RemJobs. Canonical HTML version: https://www.remjobs.works/job/periodic-labs-computational-scientist-differentiable-physics-79c941b4-6488-46b4-8568-7e336c43aadb