Member of Technical Staff — Research, Atmospheric Science

The role

This person will bring atmospheric expertise to build a Large Physics foundation Model. They will guide data sourcing, define forecast quality, and partner with research teams to translate domain knowledge into model direction.

What you will do

• Guide the sourcing and validation of atmospheric data
• Advise on observation systems, their characteristics, and their pathologies
• Define what forecast quality means
• Bring rigorous verification methodology to how the model is evaluated
• Run case studies on high-impact events to probe model behavior and surface failure modes
• Benchmark against operational numerical weather prediction baselines and the state of the field
• Partner with model, evaluation, and product teams

Required qualifications

• Deep expertise in atmospheric science, meteorology, or a closely related field (typically a PhD or equivalent research experience)
• Familiarity with operational forecasting, numerical weather prediction, and forecast verification methods
• Comfort working with large observational and reanalysis datasets
• Ability to collaborate closely with ML researchers and translate domain knowledge into technical requirements
• A rigorous, evidence-driven approach to evaluating model quality

About the company

Causal's mission is general causal intelligence. The founding team has built and deployed AI against the physical world at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.