Computational Scientist, Structural & Thermal

Periodic Labs; Menlo Park (CA); On-site; USD 225,000 - 325,000; Full time

Job summary

Periodic Labs is seeking a Computational Scientist to build structural, thermal, and coupled thermo‑mechanical simulations for semiconductor systems and advanced materials.

You’ll develop agent‑based models, extend custom solvers, and validate against wafer metrology, curvature, and other measurements, while integrating simulations into AI‑driven workflows for process optimization.

Qualifications

• PhD or equivalent in mechanical engineering or related field.
• Strong foundation in solid mechanics and heat transfer.
• Hands-on experience writing or modifying FEM codes.
• Experience developing custom solvers for structural or thermal problems.
• Familiarity with open-source frameworks such as FEniCS, deal.II, MOOSE.
• Proficient in Python; C++ is a plus.

Responsibilities

• Develop agent-based structural and thermal simulation capabilities for wafer stress, warpage, and related problems.
• Build or extend custom solvers when commercial tools are too slow or inflexible.
• Model elasticity, plasticity, viscoelasticity, creep, diffusion, and thermal expansion.
• Couple structural models to thermal, fluid, or chemical physics as needed.
• Validate models against wafer metrology, curvature, DIC, and other data.
• Design datasets with RL researchers to train LLMs for simulation pipelines.
• Generate simulated datasets for ML training where experiments are costly.
• Build scalable simulation pipelines and integrate outputs with data infra and ML systems.
• Integrate mechanics and thermal models into AI-driven workflows for process optimization.

Skills

Python

C++

Solid mechanics

Computational mechanics

FEM

Education

PhD or equivalent in mechanical engineering or related field

Tools

FEniCS

deal.II

MOOSE

Job description

About Periodic Labs

Periodic Labs is an AI and physical sciences company building state‑of‑the‑art models to accelerate breakthroughs across materials, energy, semiconductors, and beyond. Backed by world‑class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.

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 develop structural, thermal, and coupled thermo‑mechanical simulation capabilities for semiconductor systems and advanced materials.

This role is for someone who thinks of themselves as both a scientist and a software engineer. You understand solid mechanics and heat transfer at a level that goes beyond configuring a commercial FEA package, and you are comfortable building, extending, or automating solvers when the physics or scale of the problem requires it.

What You’ll Do

• Develop agent‑based structural and thermal simulation capabilities for problems central to Periodic Labs’ work: wafer stress and warpage, thin‑film residual stress, thermo‑mechanical reliability, thermal budget modeling, process‑induced deformation, fracture and delamination, and coupled heat‑stress problems in semiconductor structures.

• Build or extend custom solvers where commercial FEA tools are too slow, too opaque, or insufficiently flexible. This may include custom FEM implementations, phase‑field fracture models, crystal plasticity codes, thin‑film mechanics frameworks, or reduced‑order mechanical models, written in Python, C++, or Julia.

• Model materials behavior at the level the physics requires: elasticity, plasticity, viscoelasticity, creep, fracture, diffusion‑induced stress, thermal expansion mismatch, interfacial mechanics, and materials evolution under process conditions.

• Couple structural models to thermal, fluid, or chemical physics as the problem demands.

• Validate models against experimental measurements including wafer metrology, curvature and bow measurements, DIC, profilometry, nanoindentation, or failure analysis data.

• Design and curate evaluation datasets in collaboration with RL researchers to train LLMs capable of directing complex simulation pipelines.

• Generate simulated datasets for ML training in regimes where experimental coverage is expensive or difficult to achieve.

• Build and automate simulation pipelines at scale. Not just setting up individual runs, but architecting workflows that connect simulation outputs to data infrastructure, ML systems, and autonomous experimentation loops.

• Integrate mechanics and thermal models into Periodic Labs’ AI‑driven workflows so that simulations become active tools for process optimization and engineering decision‑making.

You Will Thrive Here If You Have

• Periodic Labs is an early‑stage startup, and we’re looking for someone who can bring technical leadership to modeling structural and thermal behavior in semiconductor devices, not necessarily someone who already has every skill listed below. A strong growth mindset, demonstrated ownership, and a track record of getting up to speed quickly in new technical areas are more important than experience in semiconductors.

• A PhD or equivalent research experience in mechanical engineering, materials science, aerospace engineering, or a closely related field, with a strong foundation in solid mechanics and heat transfer. Strong early‑career candidates with a demonstrated upward trajectory are encouraged to apply.

• Hands‑on experience with computational mechanics at the code level: writing or substantially modifying FEM codes, implementing constitutive models, or developing custom solvers for structural or thermal problems.

• Familiarity with open‑source simulation frameworks such as FEniCS, deal.II, MOOSE, or similar is a strong positive signal. Contributions to open‑source projects that others actually use are even better.

• Strong Python skills. Proficiency in C++ is a plus. Experience running simulations programmatically at scale, not just point‑and‑click.

• Deep understanding of solid mechanics theory: continuum mechanics, tensor formulations, constitutive modeling, variational methods, and numerical methods for PDEs.

• Experience using mechanics or thermal simulations to explain experimental observations, guide materials or process decisions, or surface failure mechanisms that were not obvious from measurement alone.

• Genuine curiosity about AI and a desire to work at the boundary of simulation and machine learning, not because it is required, but because you find it interesting.

Strong Candidates May Also Have

• Experience with GPU‑accelerated FEM, reduced‑order mechanical models, surrogate models, or physics‑informed neural networks for structural problems.

• Familiarity with multiscale mechanics: bridging from atomistic or DFT‑informed potentials through molecular dynamics, mesoscale methods, and continuum mechanics.

• Deep expertise in thin‑film mechanics: residual stress, Stoney equation regimes and their limits, film‑substrate interactions, delamination, and stress evolution during deposition.

• Experience with wafer‑scale mechanics: bow, warp, thermal cycling reliability, packaging‑induced stress, or interconnect mechanics in advanced packaging contexts.

• Background in fracture mechanics or damage modeling: LEFM, cohesive zone models, phase‑field fracture, fatigue, or statistical failure models.

• Knowledge of semiconductor metrology, failure analysis, or process integration that gives physical intuition for how models connect to real manufacturing decisions.

Mechanics:

• Minimum education: bachelor's degree or an equivalent combination of education and training or experience

• Location: Our lab is located in Menlo Park and we prefer folks to be located in Menlo Park or San Francisco but can be flexible based on role

• Compensation: The annual compensation range for this role - $225,000-325,000

• Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process with our legal support.

We're building a team of the world's best — the scientists, engineers, and problem‑solvers who don't just follow the frontier, they define it. If you're driven to bring AI to life in the physical world and make discoveries that have never been made before, you belong here.