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Member of Technical Staff — Research, Operations & Decision Science

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

About the role

Role focused on defining objectives, constraints, and evaluation methods for reasoning models in high-stakes operational environments, bridging research with real-world decisions in a Large Physics foundation model context.

Responsibilities

  • Formulate the objectives, constraints, and decision problems that our reasoning models optimize toward
  • Develop methodology for evaluating decision quality under uncertainty, including counterfactual reasoning about outcomes
  • Translate the realities of complex operational environments into well-posed optimization and decision problems
  • Bring rigor to how optimization and decision-making models are validated for real-world use
  • Partner with reasoning, evaluation, and product teams to connect research to the decisions it ultimately informs

Qualifications

  • Deep expertise in operations research, decision science, or a closely related field (typically a PhD or equivalent experience)
  • Strong grasp of optimization and decision-making under uncertainty, ideally including stochastic methods
  • Experience in high-stakes operational settings where forecasts drive consequential decisions
  • Particular strength in evaluating the quality of optimization or decision models, not just building them
  • Ability to collaborate closely with ML researchers and translate operational realities into technical problems

Skills

Required:Operations researchOptimization under uncertaintyDecision quality evaluationCross-disciplinary collaboration
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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