Member of Technical Staff — ML Research, Multimodal

Causal Labs; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Causal Labs in San Francisco is seeking researchers to design architectures and training recipes that turn multimodal physical observations into models that predict the future of the physical world. You will tackle sparse sensors, point clouds, and hyperspectral data at scale, pushing beyond current LLM-oriented approaches.

Join a team obsessed with rigorous experimentation, ablations, and end-to-end delivery—from data pipelines to training runs—balancing scientific curiosity with practical

Qualifications

• Strong foundations in ML with depth in at least one relevant domain.
• Experience training large-scale models and analyzing results.
• Familiarity with distributed training and scaling systems.
• Proven track record turning research into production models.

Responsibilities

• Design and implement novel model architectures for multimodal physical data.
• Solve core modeling problems for physical prediction, incl. long-horizon forecasting.
• Run experiments and ablations to link data choices to predictive performance.
• Work across ML stack from data to infrastructure to scale training runs.
• Stay up-to-date with research and bring new ideas to the project.

Skills

ML fundamentals

World models / physics-informed NNs

Large-scale model training

Distributed training

Research to production

Job description

Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.

We look for researchers who are excited to tackle unsolved problems. Predicting how physical systems evolve means learning from observations that language and vision models were not built for — sparse sensors, point clouds, hyperspectral imagery, physical fields — at a scale that dwarfs what is used to train even today's frontier LLMs. Your mission is to design the architectures and training recipes that turn these multimodal observations into a model that predicts the future of the physical world.

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

What we're looking for

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

• 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 studiesn
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• Familiarity with distributed training and the systems considerations of scaling models
• A track record of turning open-ended research problems into production models