Machine Learning Engineer, Intelligent Sensing Technology - Incubation

We're looking for a creative ML Research Engineer to join our incubation team, where you'll work across the full stack, from model training and systems integration to rapid prototyping. While working on a diverse portfolio of exploratory projects, you'll bring deep practical knowledge of ML/AI architectures and multimodal sensing applied to physical spaces, paired with a design-centric approach to moving ideas from experiment to integrated system. The ideal candidate is energized by open questions, comfortable navigating ambiguity, quick to reorient when new data shifts the direction, and always able to clearly articulate the motivation, tradeoffs, and risks behind their approach. BS and a minimum of 3 years relevant industry experience in machine learning or AI engineering Familiarity with state-of-the-art architectures including transformers, reinforcement learning, and predictive inference Strong coding skills across modern ML frameworks (e.g. PyTorch), with a practical approach to tooling that includes AI-assisted development as a natural part of the workflow Proven experience building end-to-end ML pipelines, from data acquisition and preprocessing through training, evaluation, and deployment Experience architecting and integrating sensing systems that fuse multimodal signals (e.g. vision, audio, IMU, LiDAR) with ML models running on mobile, wearable, or robotic platforms MS or PhD with substantial applied research experience in a relevant area Ability to clearly communicate technical tradeoffs, risks, and rationale to both technical and non-technical collaborators Demonstrated comfort with ambiguity and a track record of adapting quickly when direction shifts Experience working in a research, incubation, or early-stage exploratory environment Familiarity with on-device or edge ML deployment and its associated constraints Background in robotics, embedded systems, or real-time sensing pipelines Knowledge of practical Bayesian reasoning and methods Experience with physics-based machine learning — including physics-informed neural networks, simulation-to-real transfer, or learned physical models Cross-disciplinary collaboration experience — hardware, software, design, and research

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