Member of Technical Staff — Research, Operations & Decision Science

The role

This person will formulate objectives, constraints, and decision problems for reasoning models. They will develop methodologies for evaluating decision quality under uncertainty and partner with research and product teams.

What you will do

• 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

Required 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
• 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

Nice to have

• stochastic methods

About the company

Causal is building a Large Physics foundation Model (LPM) to achieve general causal intelligence. The founding team has built and deployed AI at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.