Member of Technical Staff – ML Research, Multimodal

About the role

Join causal’s mission to build general causal intelligence by creating a Large Physics foundation Model (LPM). You will work on cutting‑edge multimodal AI that predicts the future of physical systems and suggests actions to alter outcomes.

Key responsibilities

• Design and implement novel model architectures and training algorithms for massive, multimodal physical data.
• Solve core modeling challenges such as encoding heterogeneous, irregularly‑sampled modalities, stable long‑horizon rollouts, and probabilistic forecasting.
• Run experiments and ablations to connect data and modeling decisions with predictive performance.
• Work across the full ML stack—data, model, evaluation, and infrastructure—to move ideas from prototype to large‑scale training runs.
• Stay current with research literature and integrate new ideas into the product pipeline.

Required profile

• Relentless problem‑solver with rapid execution and the ability to learn quickly in unfamiliar domains.
• Strong grasp of machine‑learning fundamentals and depth in at least one relevant area (e.g., sequence/world models, computer vision, sensor fusion, generative modeling, physics‑informed NNs).
• Experience training large‑scale models and conducting careful analysis and ablation studies.
• Familiarity with distributed training and the systems considerations of scaling models.
• Track record of turning open‑ended research problems into production‑ready models.

Required skills

• Machine learning
• Sequence modeling
• World models
• Computer vision
• Sensor fusion
• Generative modeling
• Physics‑informed neural networks
• Large‑scale model training
• Distributed training
• Probabilistic forecasting