Member of Technical Staff — Research, Operations & Decision Science

Kindredventures; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Kindredventures is building a Large Physics foundation Model focused on causal intelligence to predict and influence the future in real-world environments. The role targets researchers who define objectives and evaluation methods for decision models in high-stakes settings.

We seek experts in operations research and decision science with PhD-level or equivalent background who can translate operational realities into rigorous optimization problems and collaborate across ML and product teams to

Qualifications

• PhD or equivalent experience in operations research, decision science, or related field.
• Strong understanding of optimization and decision-making under uncertainty.
• Experience in high-stakes operational settings where forecasts drive decisions.
• Ability to evaluate optimization/decision models, not only build them.
• Ability to collaborate with ML researchers and translate realities into technical problems.

Responsibilities

• Formulate objectives, constraints, and decision problems for reasoning models.
• Develop methodology for evaluating decision quality under uncertainty, including counterfactuals.
• Translate complex operational realities into well-posed optimization problems.
• Provide rigor in validation of optimization and decision-making models for real-world use.
• Partner with reasoning, evaluation, and product teams to connect research to decisions.

Skills

Operations research

Decision science

Optimization under uncertainty

Stochastic methods

High-stakes forecasting

Model evaluation

Collaboration with ML researchers

Education

PhD or equivalent experience

Job description

Overview

Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.

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 for

We 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