Startups · AI

Member of Technical Staff — ML Research, Interpretability

Causal · San Francisco · On-site

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About Causal

Backed by Accel.

About the role

Probe the model's internal representations for physical quantities, structure, and conservation laws Develop methods to explain individual predictions and the model's reasoning about interventions

What they're looking for

  • We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains
  • Strong grasp of machine learning fundamentals and the internals of modern neural network architectures
  • Experience or strong interest in interpretability, representation analysis, or related research
  • Strong engineering skills for building interpretability tooling and running careful experiments
  • A rigorous, hypothesis-driven approach to understanding model behavior
  • A track record of turning open-ended research questions into concrete findings
More about this role

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.

We look for researchers who are excited to tackle unsolved problems. Our mission is to build models capable of learning the underlying causal structures of physical systems and...

Read the full posting on Causal's site ↗

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