Machine Learning Applied Researcher

​​​​​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.