Member of Technical Staff — ML Research, Interpretability

causal· Research
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📍 San FranciscoFullTime

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 interpretability is how we will know whether they have. Before anyone acts on a model's prediction or its recommended intervention, we need to understand what the model has actually learned. Your mission is to open the model up: to understand its internal representations, explain its outputs, and build the trust that acting on physical systems demands.

Responsibilities

  • 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

  • Investigate whether interventions in the model's internal state produce physically coherent responses

  • Build tools and techniques for debugging model failures and understanding rollout behavior

  • Partner with model, evaluation, and domain teams to turn interpretability findings into better models and greater trust

What we'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

 

Frequently Asked Questions

Is the salary disclosed for the Member of Technical Staff — ML Research, Interpretability position at causal?
The salary for this Member of Technical Staff — ML Research, Interpretability role at causal is not publicly listed. Click "Apply Now" to learn more about the compensation package on their official careers page.
Where is the Member of Technical Staff — ML Research, Interpretability position at causal located?
This Member of Technical Staff — ML Research, Interpretability role at causal is based in San Francisco. The position is listed as on-site or hybrid. Check the full job description or apply directly to confirm the work arrangement.
Is the Member of Technical Staff — ML Research, Interpretability role at causal full-time or part-time?
This is listed as a FullTime position. It is posted as a Member of Technical Staff — ML Research, Interpretability role in the Research department at causal.
Which team or department does the Member of Technical Staff — ML Research, Interpretability at causal belong to?
This Member of Technical Staff — ML Research, Interpretability position is part of the Research department at causal. See the full job description for more information about the team structure and responsibilities.
How do I apply for the Member of Technical Staff — ML Research, Interpretability position at causal?
Click the "Apply Now" button on this page. You will be redirected to causal's official application portal hosted on ashby where you can submit your application directly.
When was the Member of Technical Staff — ML Research, Interpretability job at causal posted?
This Member of Technical Staff — ML Research, Interpretability position at causal was posted on Jul 20, 2026. Apply as soon as possible — early applications are often reviewed first.
Member of Technical Staff — ML Research, Interpretability
causal
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