Computational Scientist, Structural & Thermal

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 semiconductor systems, including 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.
• 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.
• 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, architecting workflows that connect simulation outputs to data infrastructure, ML systems, and autonomous experimentation loops.

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 much 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. Early-career candidates with strong research or engineering output 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, with C++, Julia, or Fortran proficiency a plus. Experience running simulations programmatically at scale rather than through point-and-click tools.
• 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.
• 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 similar experience
• Location
• : Menlo Park, CA