Member of Technical Staff — Research, Physics

Kindredventures; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Kindredventures is building a Large Physics foundation Model (LPM) to understand and control physical systems, starting with weather. We seek domain experts excited to tackle unsolved problems and to ensure the model evolves toward grounding in physical law and broad generalization.

Your mission is to bring physical principles to bear on the model, develop evaluations for physical coherence, and advise on the physics of complex systems while collaborating with ML researchers to translate physics

Qualifications

• Deep expertise in physics (fluid dynamics, thermodynamics, computational physics, or related field).
• Familiarity with numerical simulation of physical systems (e.g., CFD) and its trade-offs.
• Interest in where machine learning and physical modeling meet.
• Ability to collaborate with ML researchers and translate physical principles into technical requirements.
• Rigorous, evidence-driven approach to evaluating model quality.

Responsibilities

• Bring physical principles to bear on the model—assessing consistency with conservation laws and physical constraints.
• Develop evaluations that test whether the model's behavior is physically coherent, not just statistically accurate.
• Advise on the physics of the systems modeled (from fluid dynamics to thermodynamics) and their numerical treatment.
• Investigate generalization of the LPM across physical domains and identify breakdowns.
• Partner with model, evaluation, and interpretability teams to connect physical understanding to research direction.

Skills

Physics expertise

CFD

ML for physics

Collaboration

Evaluation mindset

Education

PhD or equivalent research experience

Job description

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. Our thesis is that scaling on physics yields a model capable of understanding the causal structure to predict and alter the future. Your mission is to ensure the model evolves towards this thesis: grounded in physical law, evaluated against it, and ready to generalize across domains.

Responsibilities

• Bring physical principles to bear on the model — assessing consistency with conservation laws and physical constraints, and where physics-informed inductive biases help or hinder

• Develop evaluations that test whether the model's behavior is physically coherent, not just statistically accurate

• Advise on the physics of the systems we model, from fluid dynamics to thermodynamics, and their numerical treatment

• Investigate where the LPM generalizes across physical domains and where it breaks down

• Partner with model, evaluation, and interpretability teams to connect physical understanding to research direction

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 physics — fluid dynamics, thermodynamics, computational physics, or a closely related field (typically a PhD or equivalent research experience)

• Familiarity with numerical simulation of physical systems (e.g. CFD) and its trade-offs

• Interest in where machine learning and physical modeling meet

• Ability to collaborate closely with ML researchers and translate physical principles into technical requirements

• A rigorous, evidence-driven approach to evaluating model quality