> Markdown version of [/jobs/ext/3075803-job-descriptionmachine-learning-researcher](https://www.wearedevelopers.com/jobs/ext/3075803-job-descriptionmachine-learning-researcher). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Job DescriptionMachine Learning Researcher

- **Company:** Causal Labs
- **Location:** San Francisco, CA, United States
- **Salary:** $200,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Computational Fluid Dynamics, Computer Simulation, Distributed Computing Environment, Language Modeling, Sensor Fusion, Graphics Processing Unit (GPU), Data Pipelines
- **Published:** September 25, 2026
- **Apply:** https://www.thejobnetwork.com/job/06994dde-1578-4ca7-a22b-d7db55a913d3/machine-learning-researcher

## About the Role

* 1+ years training large-scale foundation models from scratch (pre-training ideal), not only fine-tuning
 * Strong ML fundamentals and depth in at least one core domain (computer vision, sensor fusion, language models, physics-informed networks)
 * Distributed training and inference across hundreds to thousands of GPUs
 * Petabyte-scale data pipelines for model training
 * A mission-driven passion for science or the physical world
 * Individual-contributor focus
 * Ability to work in person in San Francisco 5 days a week

Nice to Have

 * Research lab experience (e.g. DeepMind, FAIR)
 * PhD or Master's in a quantitative field; background in physics or related sciences
 * Meteorology, computational fluid dynamics or numerical simulation

## Description

Causal Labs is hiring ML researchers to build powerful physics models grounded in observable feedback and verifiable ground truth. If you have done frontier research and trained large-scale models from scratch in language, vision, robotics or biology, you will work across the full ML stack to build a Large Physics foundation Model, starting with weather.

What You Will Do

 * Work across the full ML stack: data, model, evaluation and infrastructure.
 * Implement novel model architectures and training algorithms.
 * Build data pipelines and training infrastructure for massive, petabyte-scale, multimodal datasets.
 * Iterate rapidly on experiments and ablations, and read results through careful analysis.
 * Bring new ideas from current research into the work.