Machine Learning - Research

causal· Engineering
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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 optimal actions to change that future.

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because domains governed by physics have inherent cause and effect relationships, unlike visual or textual data.

Weather is the ideal training ground for an LPM. It is the most well-observed physical system, offering rapid, objective ground truth feedback from sensory observations and data at a scale that dwarfs what is used to train today’s LLMs.

Causal Labs is a team of researchers and engineers from self-driving, drug discovery, and robotics - including Google DeepMind, Cruise, Waymo, Insitro, and Nabla Bio - who believe general causal intelligence will be the most important technical breakthrough for civilization.

We look for researchers who are excited to tackle unsolved problems.

Our research challenges offer an opportunity to build powerful models grounded in observable feedback and verifiable ground truth. If you have experience doing frontier research and training large-scale models from scratch in related fields such as language and vision models, robotics, biology – join us.

Responsibilities

  • Work across the full ML stack (data, model, eval, and infrastructure)

  • Implement novel model architectures and training algorithms

  • Build data pipelines and training infrastructure for massive, petabyte-scale, multimodal datasets

  • Rapidly iterate on experiments and ablations

  • Stay up-to-date on research to bring new ideas to work

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 depth in at least one core domain (e.g. Computer Vision, Sensor Fusion, Language Models, Physics-informed NNs)

  • Experience training models and an ability to understand experiment results through careful analysis and ablation studies.

  • Experienced at writing and optimizing massive petabyte-scale data pipelines.

  • Familiarity with distributed training and inference.

  • [bonus] Familiarity with meteorology, computational fluid dynamics, and/or numerical simulations.

You don’t have to meet every single requirement above.

Frequently Asked Questions

Is the salary disclosed for the Machine Learning - Research position at causal?
The salary for this Machine Learning - Research 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 Machine Learning - Research position at causal located?
This Machine Learning - Research 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 Machine Learning - Research role at causal full-time or part-time?
This is listed as a FullTime position. It is posted as a Machine Learning - Research role in the Engineering department at causal.
Which team or department does the Machine Learning - Research at causal belong to?
This Machine Learning - Research position is part of the Engineering department at causal. See the full job description for more information about the team structure and responsibilities.
How do I apply for the Machine Learning - Research 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 Machine Learning - Research job at causal posted?
This Machine Learning - Research position at causal was posted on Oct 29, 2025. Apply as soon as possible — early applications are often reviewed first.
Machine Learning - Research
causal
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