Machine Learning Researcher

Transparent Search Group • San Francisco, CA

23h • Onsite

About The Position

Causal Labs is pursuing general causal intelligence: AI that can predict the future and identify the actions that change it. It is building a Large Physics foundation Model (LPM), because domains governed by physics have inherent cause-and-effect structure that visual or textual data lacks. Its starting domain is weather, the most observed physical system on earth, with rapid ground-truth feedback and data volumes that dwarf LLM training sets. The founders come from Cruise, Google Research and Meta. The company is about 10 people in San Francisco, growing to around 35 this year, and is backed by Kindred Ventures, Refactor and BoxGroup.

Requirements

• 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 Haves

• 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

Responsibilities

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior