Machine Learning Researcher

Company: Causal Labs
Location: San Francisco CA (South Park office in person 5 days per week)
Compensation: $200000 base highly competitive early-stage equity
Employment Type: Full-time
Visa Sponsorship: Visa transfers; can sponsor visas

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.

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.

• 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.

• 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

• Research lab experience (e.g. DeepMind FAIR)
• PhD or Masters in a quantitative field; background in physics or related sciences
• Meteorology computational fluid dynamics or numerical simulation

Initial screen culture interview (30 min) technical screen onsite day.

Python PyTorch Weights & Biases large-scale GPU clusters (100s-1000s of GPUs)

REVENUE: 14% of first-year salary. Est. fee per hire $28K-$28K; 5 seat(s) up to $140K if all filled.

TARGET COMPANIES (suggested (physical AI / weather)): Google DeepMind NVIDIA (Earth-2) Waymo Cruise Aurora Isomorphic Labs Microsoft Research (Aurora weather).

BEST-FIT CANDIDATE: 1 (post-PhD/MS) yrs; from-scratch pre-training at scale; 100s-1000s GPU distributed training; petabyte data pipelines; visa: transfers; can sponsor; location: SF 5 days. Physics/science research passion required; avoid fine-tune-only product-ML and managers unwilling to be ICs.