Member of Technical Staff — Research, Atmospheric Science
Causal Labs· Research
San Francisco, SingaporeFull-timePosted 2mo ago
About the role
Research-focused role applying deep atmospheric science expertise to guide data sourcing, model evaluation, and integration with ML researchers within a Large Physics foundation Model project focused on weather and causal intelligence.
Responsibilities
- Guide the sourcing and validation of atmospheric data, advising on observation systems, their characteristics, and their pathologies
- Define what forecast quality means, bringing rigorous verification methodology to how we evaluate the model
- 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 to translate atmospheric expertise into research direction and credible results
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
Skills
Required:Atmospheric science expertiseOperational forecasting / numerical weather predictionData handling (observational/reanalysis datasets)Collaboration with ML researchersModel evaluation & verification methodologies
Last updated Oct 2, 2026. Interested? Apply on the company site.
About Causal Labs
Towards general causal intelligence
Causal Labs is building a Large Physics foundation Model (LPM) to learn causality through physics and weather
causalityphysicsweatherAIfoundation model
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