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Member of Technical Staff — ML Research, Planning

Causal Labs· Research
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San Francisco, SingaporeFull-timePosted 2mo ago

About the role

Research and build planning and decision-making capabilities on top of a predictive physics foundation model to enable interventional causality in high-dimensional physical systems, starting with weather.

Responsibilities

  • Research and implement methods that turn a predictive physics model into one that reasons toward objectives — 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
  • Build interfaces for specifying objectives and constraints, and methods for producing actions that satisfy them
  • Run experiments and ablations that connect reasoning methods to decision quality
  • Work across the full ML stack — data, model, eval, and infrastructure — to take ideas from prototype to scaled training runs

Qualifications

  • Strong grasp of machine learning fundamentals, with 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 and the 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
Last updated Oct 2, 2026. Interested? Apply on the company site.

About Causal Labs

Towards general causal intelligence

Causal Labs is building a Large Physics foundation Model (LPM) to learn causality through physics and weather

causalityphysicsweatherAIfoundation model
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