Machine Learning Researcher

Job Description

Job Description

Machine Learning Researcher

Company: Confidential — venture-backed technology company
Location: San Francisco, CA (South Park office, in person 5 days per week)
Compensation: $200,000 base + highly competitive early-stage equity
Employment Type: Full-time
Visa Sponsorship: Visa transfers; can sponsor visas

About the Company

The client 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.

The Role

The client 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.

What You Bring

• 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

Interview Process

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

Tech Stack

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

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