Member of Technical Staff, AI Research

Role and Responsibilities

• Build and train AI agents and training systems that learn to do physics.
• Focus on core research questions such as how agents acquire physical reasoning, how to design action spaces for scientific tool use, how to structure rewards that survive long-horizon discovery tasks, and how training infrastructure scales without breaking the science.
• Design evaluation workflows and benchmarks for physics reasoning. Distinguish genuine reasoning from pattern matching and benchmark gaming.
• Build the instrumentation that makes agent behavior interpretable, not opaque.
• Publish results that advance the field of AI for science.
• Develop training curricula, reward structures, and architectures for discovery tasks; iterate on what works in practice; share what works at top ML venues where it serves the mission.
• Collaborate with physicists who design verification harnesses and with engineers who build training infrastructure.
• Ship working systems end-to-end, not isolated research artifacts.

What We're Looking For

• PhD in machine learning, computer science, physics, mathematics, or a related quantitative field, with a track record of recent publications at top venues (NeurIPS, ICML, ICLR, or comparable physics-ML venues).
• You have produced original research that the community recognizes.
• Hands-on track record building agents and training models with reinforcement learning, ideally for science, mathematics, code, or other complex-reasoning domains.
• You have shipped working RL systems that beat non-trivial baselines, with rigorous experimental methodology.
• Proficiency with modern ML frameworks and distributed training.
• You can move from a single GPU to a cluster without rewriting your code, and you understand what breaks at each scale.
• A physics or mathematics background providing intuition for physical reasoning and scientific tool use.
• You can hold a substantive conversation with a domain physicist.
• Nice to Have:
• Hands-on experience with modern RL algorithms (PPO, SAC, MuZero, multi-agent self-play, search-augmented methods, or comparable).
• Deep fluency with PyTorch or JAX, plus distributed training via Ray, XLA, Accelerate, or comparable.
• Experience applying agents to simulators, scientific tools, games, or rigorous benchmark suites.
• Open-source contributions, conference presentations, or shipped research artifacts that the community has adopted.

How We Work

• We are engineering-led. Engineers and researchers own problems end-to-end, 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: engineers work with agentic coding tools daily, write specs that are legible to humans and agents alike, and lead with leverage.

Location and Compensation

• This role is based in Boston. We will consider remote candidates on a case-by-case basis.
• We offer competitive compensation including salary, benefits, and meaningful early-stage equity.
• We evaluate on technical breadth, systems thinking, scientific curiosity, and shipping velocity.

About the Role

This role is based in Boston. We will consider remote candidates on a case-by-case basis. We offer competitive compensation including salary, benefits, and meaningful early-stage equity. We evaluate on technical breadth, systems thinking, scientific curiosity, and shipping velocity. We are an equal opportunity employer and value diverse perspectives in building platforms for AI-driven discovery.