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Description
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.
Minimum Qualifications
BS and a minimum of 3 years relevant industry experience in machine learning or AI engineeringFamiliarity with state-of-the-art architectures including transformers, reinforcement learning, and predictive inferenceStrong 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 workflowProven experience building end-to-end ML pipelines, from data acquisition and preprocessing through training, evaluation, and deploymentExperience 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
Preferred Qualifications
MS or PhD with substantial applied research experience in a relevant areaAbility to clearly communicate technical tradeoffs, risks, and rationale to both technical and non-technical collaboratorsDemonstrated comfort with ambiguity and a track record of adapting quickly when direction shiftsExperience working in a research, incubation, or early-stage exploratory environmentFamiliarity with on-device or edge ML deployment and its associated constraintsBackground in robotics, embedded systems, or real-time sensing pipelinesKnowledge of practical Bayesian reasoning and methods Experience with physics-based machine learning — including physics-informed neural networks, simulation-to-real transfer, or learned physical modelsCross-disciplinary collaboration experience — hardware, software, design, and research
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