Location: Palo Alto, California (in person)

Compensation: $220,000 - $300,000 base salary + equity

Relocation: Relocation assistance available

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About Primateria

Primateria is an AI-native scientific research lab.

We are building AI systems that turn scientific questions into computationally testable problems, run the necessary experiments, learn from failed runs, and recursively work toward answers.

Our current work starts with the many-electron Schrödinger equation. We are developing transferable neural wavefunction models for difficult chemical systems, including open-shell transition-metal chemistry, and connecting them to molecular simulation, machine learned potentials, and eventually autonomous scientific discovery.

The role

You will own scientific judgment around our electronic structure and molecular simulation work.

You will choose scientific problems, establish reference calculations, diagnose failures, and design experiments that distinguish genuine progress from maximizing benchmark performance.

Initially, you will work on difficult electronic structure problems involving transition metals, strong correlation, bond dissociation, and transferable neural wavefunctions. Over time, this may expand into learned potentials, molecular dynamics, reaction chemistry, materials, and biomolecular simulation.

What you will do

• Design calculations and benchmarks that can decisively validate or falsify new methods.
• Work on challenging open-shell, transition-metal, and strongly correlated systems.
• Determine appropriate reference methods and understand where those references themselves break down.
• Diagnose failures involving optimization, Hamiltonians, ECPs, basis sets, spin states, sampling, estimators, or model architecture.
• Evaluate energies, forces, densities, spin densities, dipoles, and other relevant observables.
• Help connect high-accuracy wavefunction calculations to MLIPs and larger-scale molecular simulation.
• Work with our engineering team to turn expert scientific reasoning into tools, evaluators, and increasingly autonomous research workflows.