Member of Technical Staff — ML Research, Planning
On-siteSan Francisco, California, United States
Job Summary
Research and implement methods that turn a predictive physics model into one that reasons toward objectives, focusing on planning, control, and decision-making against a learned model of the world. Develop approaches for decision-making under uncertainty in high-dimensional, continuous physical state spaces and build interfaces for specifying objectives and constraints to produce satisfying actions. Run experiments and ablations that connect reasoning methods to decision quality while working across the full ML stack—from data and model training to evaluation and infrastructure—to scale prototypes to large training runs. The role targets unsolved problems in interventional causality for a Large Physics foundation Model, requiring a track record of turning open-ended research into working systems.
Required Qualifications
- Strong grasp of machine learning fundamentals
- Depth in at least one relevant area (e.g. reinforcement learning, planning and control, decision-making under uncertainty, model-based RL, 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
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