Machine Learning Engineer

WHAT YOU WILL DO: Build multimodal perception models that make robots socially aware, such as active speaker detection, person tracking across camera and lidar, and engagement estimationBuild and scale the pipelines that turn multi-hour, full-stack robot logs into curated, labeled datasets for training, evaluation, and external data partnershipsShip models as containerized services in our OM1 deployment stack, and own their latency, reliability, and regressions in the fieldWork closely with our research lab, robotics engineers, and deployment teams to move models from prototype to production on physical robots within weeksIntegrate, fine-tune, and optimize foundation models for real-time perception and reasoning on embedded computeWHAT WE LOOK FORMS or PhD in machine learning, robotics, computer science, or a related field 1+ years of industry or applied research experience training and shipping machine learning modelsStrong engineering fundamentals in languages such as Python, Go, and/or C++, and fluency in PyTorch or JAXHands‑on experience with multimodal models, perception, or reinforcement and imitation learningExperience running models on edge hardware under real latency and memory constraints, such as the NVIDIA Jetson software stackExperience with TensorRT, ONNX, or other inference optimization toolchainsExperience with ROS2 and Docker‑based deployment on robotsExperience building datasets or benchmarks used outside your own teamPublications at CoRL, RSS, ICRA, NeurIPS, ICML, or ICLRA bias toward models that work in the real world, not only on benchmarks