Member of Technical Staff — ML Research, Planning

What you'll do

• Develop planning, control, and decision-making methods for learned physics models, including under uncertainty in high-dimensional continuous state spaces.
• Build objective and constraint interfaces and methods for generating actions that satisfy them; evaluate decision quality through experiments and ablations.
• Work across data, models, evaluation, and infrastructure to take research ideas from prototype to scaled training runs.

What they ask for

• Strong grasp of machine learning fundamentals and 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 and analyzing experimental results through careful analysis and ablation studies.
• Familiarity with reasoning, planning, or acting with learned models.
• Track record of turning open-ended research problems into working systems.

AI focus

Build the planning layer for a Large Physics foundation Model, enabling objective-conditioned planning and action generation for physical systems.