Is the Computational Scientist, Differentiable Physics role remote?

No — this is an on-site role in Menlo Park, CA.

What's the salary range?

Periodic Labs lists $250,000–$350,000 for this role.

How much experience is required?

At least 3 years of relevant experience for this Computational Scientist, Differentiable Physics role.

Where is the role based?

Periodic Labs is hiring for this position in Menlo Park, CA.

What's the tech stack?

Joblaze extracted these technologies from the posting: C++, Deep Learning, JAX, Julia, PyTorch, Python.

Does Periodic Labs sponsor work visas for this role?

Yes — the posting indicates visa sponsorship is available for the right candidate.

What seniority level is this role?

Periodic Labs targets senior candidates for this position.

Computational Scientist, Differentiable Physics at Periodic Labs

Joblaze summary

In this role, the Computational Scientist focuses on developing differentiable solvers for complex continuum-physics simulations, particularly in fluid dynamics and multi-physics scenarios. Proficiency in numerical methods, deep learning frameworks like JAX or PyTorch, and strong programming skills are essential for success. This position is ideal for candidates with a PhD or equivalent experience in relevant fields, who possess a startup mentality and a passion for tackling real-world scientific challenges. Periodic Labs emphasizes innovation and collaboration, making it a dynamic environment for those eager to push the boundaries of computational science.

Joblaze insights

Listed 7 days ago on Joblaze — tracked directly from Periodic Labs's career page.
Periodic Labs has 29 other open roles including Don't See Your Role? Apply Here!, Environmental Health and Safety, Environmental Health & Safety (EHS) Technician - contract to hire.
6517 active Python roles on Joblaze right now.
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480 active PyTorch roles on Joblaze right now.
Salary band is above the typical range for AI/ML roles (median ~$188,000).
Pays more than 94% of the 3224 salaried Python roles we track (median $160,000). See Python salary trends

Quick facts

Is the Computational Scientist, Differentiable Physics role remote?: No — this is an on-site role in Menlo Park, CA.
What's the salary range?: Periodic Labs lists $250,000–$350,000 for this role.
How much experience is required?: At least 3 years of relevant experience for this Computational Scientist, Differentiable Physics role.
Where is the role based?: Periodic Labs is hiring for this position in Menlo Park, CA.
What's the tech stack?: Joblaze extracted these technologies from the posting: C++, Deep Learning, JAX, Julia, PyTorch, Python.
Does Periodic Labs sponsor work visas for this role?: Yes — the posting indicates visa sponsorship is available for the right candidate.
What seniority level is this role?: Periodic Labs targets senior candidates for this position.
Is this full-time or contract?: Full-time for this Computational Scientist, Differentiable Physics role at Periodic Labs.

From the original posting

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.