The role

You will invent, analyze and deploy the mathematical and algorithmic core of our design engine. Your mandate:

• Design and train physics-informed foundation models — equivariant models, conditional generative modeling, PINNs, learning non-equilibrium importance sampling, etc.
• Derive new objective functions and samplers that marry quantum-level fidelity with GPU-level throughput.
• Ship production-grade code: models that run on our cluster today, not “interesting ideas” parked on a branch.
• Collaborate with simulation physicists and wet-lab scientists to close the design-build-test-learn cycle in weeks, not quarters.

You might be a fit if you

• Hold a PhD (or equivalent experience) in Physics, Applied Math, CS, or a related field.
• Strong publication or project record in top ML or life-science venues (NeurIPS, ICML, ICLR, Nature, Science, Nature Methods, etc.)
• Have meaningful experience with multiple: atomic ML models, generative protein modeling, molecular dynamics and quantum-chemistry calculations, differential-equation modeling, enhanced sampling simulation, Monte-Carlo methods.
• Write clean, tested code and value reproducibility.
• Thrive in ambiguity, hunt down first principles, and communicate crisply across disciplines.
• Can derive a variational bound before breakfast and implement it in JAX or PyTorch before lunch.

Bonus points

• Publications on symplectic / energy-conserving neural nets, CNFs, diffusion modeling, or Boltzmann generators.
• An understanding of molecular biology, biocatalysis and/or protein science.

Why us

• Top-tier cash compensation plus generous equity.
• Hardware to match your ambitions (all the compute you need!).
• Full medical/dental, 401(k) with match, unlimited PTO.