Member of Technical Staff – ML Research & Planning

About the role

We are looking for a Member of Technical Staff to join our ML research team in San Francisco. The role focuses on building a planning layer on top of our Large Physics foundation Model, enabling the system to reason about objectives and generate actionable interventions.

Key responsibilities

• Research and implement methods that transform a predictive physics model into a planning and decision‑making system.
• Develop algorithms for decision‑making under uncertainty in high‑dimensional, continuous physical state spaces.
• Create interfaces for specifying objectives and constraints and generate actions that satisfy them.
• Run experiments and ablations to connect reasoning methods with decision quality.
• Work across the full ML stack – data, model, evaluation, and infrastructure – to move prototypes to large‑scale training runs.

Required profile

• Strong grasp of machine‑learning fundamentals with depth in at least one relevant area.
• Experience training large models and analysing experimental results through careful ablations.
• Familiarity with challenges of reasoning, planning, or acting with learned models.
• Track record of turning open‑ended research problems into working systems.

Required skills

• Reinforcement learning
• Planning and control
• Decision‑making under uncertainty
• Model‑based reinforcement learning
• Post‑training of large models