Head of Core AI

Overview

Physical Superintelligence is a startup with roots at Google, NVIDIA, Harvard, Meta, MIT, Oxford, Johns Hopkins, Cambridge, and the Perimeter Institute building AI systems to discover new physics at scale.

Our mission is to discover and commercialize transformative physics breakthroughs at scale with artificial superintelligence, safely, verifiably, and for broad public benefit.

The last century's golden age of physics gave us transistors, lasers, and nuclear energy. We believe artificial superintelligence will unlock the next one. We're creating the infrastructure to industrialize scientific discovery and usher in this new era.

We have one product: new physics, at scale.

Role

We are seeking a Head of Core AI to lead Core AI: the team that builds the models, agents, and training systems that do physics. This is applied work in service of a product. Everything Core AI builds exists to make PSI's AI-for-physics capability better.

Responsibilities

• Lead Core AI, the organization that builds the models, agents and training systems that do physics: own the people, the delivery and the hiring.
• Set the roadmap and own the research portfolio against one goal, making PSI's AI systems better at real physics: set direction, kill weak lines, double down on what works.
• Hold the work to one bar, physics a real physicist would trust, and own the training and evaluation infrastructure the company depends on to get there.
• Partner with Applied AI, which turns Core AI's work into products, and Physics, which supplies the problems and the verification; build on the platform Engineering runs.
• Stay technical: carry individual work, review code, and set the bar in person.

What We're Looking for

• Eight or more years of AI research work and a track record of leading AI research teams that shipped: you have run an industrial AI research group at a frontier lab, a fast-moving AI company, or equivalent, and the work made it into production systems or into results the field recognizes. At least three years of direct people management: hiring, performance reviews, and career development for the people who reported to you.
• Research vision on a portfolio. You pick the question and design the experiment that settles it, state the result that would kill a line and stop it when that result arrives, know when a benchmark is being gamed, and trade elegance for something that ships this quarter.
• Deep fluency across modern ML, with depth in one of the following and a working floor on the rest. You have personally built and trained systems that beat non-trivial baselines, and you can still do so.
• Reinforcement learning and post-training: policy optimization, reward design, RLHF, RLVR, GRPO, RL for language models and for control.
• Agents, evaluation and verification: tool use, long-horizon reasoning, memory and harnesses, evals, verifiers, reward models, instrumentation that separates reasoning from gaming.
• Model architectures and training methods: transformers, state-space and graph models, neural operator and surrogate architectures for physical fields and meshes, scaling laws, optimization, training dynamics.
• Scientific and generative modeling: learning from simulators, physics-informed and conservation-aware models, out-of-distribution behaviour, diffusion and generative models, code generation with checkable results.

Motivation for this specific job is assessed at the first conversation: why this over a frontier lab.

Nice to Have

• Publications and standing at top ML venues, or an AI research artifact the community has adopted.
• Enough physics or mathematics to hold a substantive conversation with domain scientists.
• Hands-on experience applying agents or RL to science, mathematics, code, or other complex-reasoning domains.
• Experience scaling an AI research organization through rapid growth: hiring, leveling, performance management.
• AI-for-science experience: simulators, verification, scientific tool use.

How We Work

• We hold a high technical bar and give people full ownership of their work, from spec to ship to on-call.
• We write contracts before logic, test against real systems instead of mocks, and favor simple designs that ship over clever ones that do not.
• Our development process is AI-native: we work with agentic coding tools daily, write specs that are legible to humans and agents alike, and lead with leverage.

Pay and benefits

This role is hybrid, three days a week in person in our Cambridge, MA office. We offer competitive compensation including salary, benefits, and meaningful early-stage equity. We evaluate on AI research judgment, technical depth, leadership track record, and shipping velocity.