Machine Learning Engineer

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. #J-18808-Ljbffr