Physics-Informed ML Research Scientist

Kindredventures; San Francisco (CA); Sur place; USD 150 000 - 230 000; Plein temps

Résumé du poste

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.

Responsabilités

• 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.

Connaissances

Physics expertise

CFD

ML for physics

Collaboration

Evaluation mindset

Formation

PhD or equivalent research experience

Description du poste

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