Member of Technical Staff — ML Research, Planning

The role

This person will build the planning layer on top of a Large Physics foundation Model. They will research and implement methods to turn a predictive physics model into one that reasons toward objectives.

What you will do

• Research and implement methods that turn a predictive physics model into one that reasons toward objectives
• Develop approaches for decision-making under uncertainty in high-dimensional, continuous physical state spaces
• Build interfaces for specifying objectives and constraints
• Build methods for producing actions that satisfy objectives and constraints
• Run experiments and ablations that connect reasoning methods to decision quality
• Work across the full ML stack including data, model, eval, and infrastructure

Required qualifications

• Strong grasp of machine learning fundamentals
• Depth in at least one relevant area such as reinforcement learning, planning and control, decision-making under uncertainty, model-based RL, or post-training of large models
• Experience training models
• Ability to understand experimental results through careful analysis and ablation studies
• Familiarity with the challenges of reasoning, planning, or acting with learned models
• A track record of turning open-ended research problems into working systems

About the company

Causal is building a Large Physics foundation Model to achieve general causal intelligence. The founding team has built and deployed AI in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.