We are building a new class of multimodal foundation models for the physical world. Our focus is on combining time series / sensor data, language, vision, audio, and other real-world signals into unified models that can understand complex systems, reason over long horizons, and support real-world tasks in industrial and physical environments. We are looking for an experienced, researcher-oriented ML candidate to help build these systems end to end: from problem formulation and experimental design, to model development, evaluation, and deployment. This role is intended for someone who is highly self-directed, can independently perform strong scientific work, and is excited to work on multimodal intelligence grounded in physical signals.
What you’ll work on
- Build and improve multimodal foundation models that incorporate time-series / sensor data alongside language, vision, audio, and related modalities
- Drive research and modeling efforts from problem definition through experimentation and evaluation
- Own modeling work across data, modeling, and evaluation
- Advance model architectures and training strategies for physical-world understanding and long-context reasoning
- Drive and scale research experiments and modeling advances to production models that power diverse use cases in complex industrial scenarios
- Contribute to research directions with potential for publication
What we’re looking for
- Self-directed and comfortable operating in ambiguous problem spaces
- Able to independently perform strong scientific work, including forming hypotheses, designing experiments, and drawing sound conclusions
- Experience with end-to-end modeling, including data, modeling, and evaluation
- Experience with productionization or deployment of ML models
- Multimodal experience preferred
- Strong technical judgment and experimental rigor
Why this role is interesting
Many important real-world systems cannot be understood from text or vision alone. Their behavior depends on signals that evolve over time: sensors, operating conditions, environment, and interactions across subsystems. We believe the next generation of useful foundation models will need to integrate these sources of information and reason over them in a unified way. You will have a unique opportunity to help shape a new generation of multimodal foundation models grounded in physical signals and real-world dynamics. The role offers a rare combination of deep research challenges, practical deployment impact, and the chance to contribute to a fast-emerging area of AI.
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