Staff ML Researcher: Planning for Causal Physics AI

Causal Labs; San Francisco (CA); On-site; USD 130,000 - 190,000; Full time

Job summary

Causal Labs in San Francisco is pursuing a Large Physics foundation Model capable of predicting the future and identifying actions to alter it. We seek researchers to build the planning layer on top of the LPM, conditioning the model on objectives and producing actions that achieve them, from operational decisions to physical interventions.

The role involves research and implementation across planning, control, and decision-making with a focus on interventional causality rather than mere

Qualifications

• Strong grounding in machine learning fundamentals.
• Experience with RL, planning, or model-based approaches.
• Ability to turn open-ended research into working systems.

Responsibilities

• Turn a predictive physics model into planning, control, and decision-making against a learned world model.
• Develop decision-making under uncertainty in high-dimensional physical state spaces.
• Build interfaces to specify objectives and constraints and produce actions.
• Run experiments and ablations linking reasoning to decision quality.
• Work across ML stack from data to training runs to scale experiments.

Skills

Reinforcement learning

Planning and control

Decision-making under uncertainty

Model-based RL

Job description

Causal Labs in San Francisco is pursuing a Large Physics foundation Model capable of predicting the future and identifying actions to alter it. We seek researchers to build the planning layer on top of the LPM, conditioning the model on objectives and producing actions that achieve them, from operational decisions to physical interventions.

The role involves research and implementation across planning, control, and decision-making with a focus on interventional causality rather than mere