Machine Learning Engineer

You will turn research into reliable models for physical robots. You will build training, evaluation, and inference systems using real-world embodied data, including video, lidar, audio, state-action logs, and reasoning traces. You will develop multimodal perception models, curate datasets, deploy containerized services, optimize models for embedded hardware, and monitor their field performance.

Responsibilities

• Build multimodal perception models for socially aware robots
• Build and scale pipelines that transform robot logs into curated and labeled datasets
• Ship models as containerized services in the OM1 deployment stack
• Own model latency, reliability, and field regressions
• Move models from prototypes to production on physical robots
• Integrate, fine-tune, and optimize foundation models for real-time perception and reasoning on embedded compute

Requirements

• MS 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 models
• Strong engineering fundamentals in Python, Go, and/or C++
• Fluency in PyTorch or JAX
• Hands-on experience with multimodal models, perception, or reinforcement and imitation learning
• Experience running models on edge hardware under latency and memory constraints
• Experience with TensorRT, ONNX, or other inference optimization toolchains
• Experience with ROS2 and Docker-based deployment on robots
• Experience building datasets or benchmarks used outside your own team
• Publications at CoRL, RSS, ICRA, NeurIPS, ICML, or ICLR

Benefits

• Equity