We look for domain experts who are excited to tackle unsolved problems. A prediction matters most when it leads to better decisions - and evaluating decision quality in high-stakes operational environments is a challenge on its own. Your mission is to bring that discipline to our reasoning research: defining the objectives our models optimize toward and the methods by which we judge whether their decisions are actually good.
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
What we're looking forWe value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
- 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
