Head of Artificial Intelligence

At The ReWork Group, we partner with high-growth startups and forward-thinking companies to build the future. Partnering with one of our growing clients, we're on the search fora Head of Artificial Intelligence who wants operate at the intersection of frontier AI research and fundamental science to unlock new scientific knowledge and deliver real-world impact.

As Head of AI, you'll own the AI research agenda, leading the design of foundation models and reinforcement learning systems that reason about simulations, generate hypotheses, and verify new physics. This is a hands-on technical leadership role with roughly a 60:40 split between engineering and strategy. We think it's ideal for someone ready to build and scale their own AI organization.

• Set and drive the AI research roadmap across foundation models, reinforcement learning, and agentic systems for physics discovery
• Contribute directly to core research: architect models, design training schemes, and prototype new agent behaviors in close collaboration with physicists and engineers
• Design training infrastructure and evaluation frameworks that enable AI systems to discover, test, and validate physics results at scale
• Recruit, hire, and manage AI researchers and engineers, building a high-bar, collaborative research culture
• Define processes for project selection, experimentation, code review, and cross-team collaboration with physics and platform groups
• Serve as the internal thought leader on AI, guiding technical trade-offs, priorities, and long-term architecture decisions

• Foundation model pretraining, RL (PPO/SAC-style methods, self-play, or similar), large-scale optimization, and distributed training across multi-GPU/multi-node systems (CUDA, NCCL, etc.).
• Demonstrated ability managing and growing small, high-performing research or engineering teams.
• Ability to set research direction, decompose ambitious goals into executable projects, and maintain high technical and communication standards.
• Experience with scientific ML (physics-informed neural networks, neural PDE solvers, differentiable simulations, or surrogate modeling).
• Prior work integrating models with large-scale simulations or complex simulators (science, robotics, games, or similar).
• Familiarity with export-control, dual-use, or safety considerations around advanced AI systems.