Member of Technical Staff — ML Research, Multimodal
Causal Labs· Research
San Francisco, SingaporeFull-timePosted 2mo ago
About the role
Researcher focused on designing architectures and training procedures to enable a Large Physics foundation Model (LPM) that can predict the future of physical systems using multimodal observations (sparse sensors, point clouds, hyperspectral imagery, physical fields) with a goal of accurate long-horizon predictions and probabilistic forecasting.
Responsibilities
- Design and implement novel model architectures and training algorithms for learning from massive, multimodal physical data
- Solve core modeling problems unique to physical prediction: encoding heterogeneous and irregularly-sampled modalities, stable long-horizon rollouts, and probabilistic forecasting
- Run experiments and ablations that connect modeling and data decisions to predictive skill, including which data sources and mixtures most improve the model
- Work across the full ML stack — data, model, eval, and infrastructure — to take ideas from prototype to scaled training runs
- Stay up-to-date on research to bring new ideas to work
Qualifications
- Strong grasp of machine learning fundamentals, with depth in at least one relevant domain (e.g. sequence or world models, computer vision, sensor fusion, generative modeling, physics-informed NNs)
- Experience training large-scale models and the ability to understand experimental results through careful analysis and ablation studies
- Familiarity with distributed training and the systems considerations of scaling models
- A track record of turning open-ended research problems into production models
Skills
Required:machine learning fundamentalslarge-scale model trainingdistributed trainingopen-ended research to production
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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