Impress employers and recruiters.
Choose from hundreds of resume examples.
Impress employers and recruiters.
Choose from hundreds of resume examples.
Target Resume Now
About the Role
We are building a Large Physics foundation Model (LPM) to achieve general causal intelligence, enabling AI to predict and alter the future of physical systems, starting with weather. Your mission is to design architectures and training recipes that transform multimodal observations into a predictive model 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, including encoding heterogeneous and irregularly-sampled modalities, stable long-horizon rollouts, and probabilistic forecasting.
- Run experiments and ablations connecting modeling and data decisions to predictive skill.
- Work across the full ML stack (data, model, eval, infrastructure) to scale ideas from prototype to production.
- Stay up-to-date on research and incorporate new ideas into the work.
Requirements
- Relentless approach to problem-solving, rapid execution, and ability to learn quickly 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 analyzing experimental results through careful ablation studies.
- Familiarity with distributed training and the systems considerations of scaling models.
- Track record of turning open-ended research problems into production models.
Skills
- Machine learning fundamentals
- Sequence models
- World models
- Computer vision
- Sensor fusion
- Generative modeling
- Physics-informed NNs
- Large-scale model training
- Distributed training
- Multimodal data processing
- Physical prediction modeling
About the Company
- Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
- 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.