Machine Learning Engineer (Robotics/ Physical AI)

About the role:
Build training data to operate on physical robotic platforms.
Own the full machine learning lifecycle—from data collection and custom architecture design to edge deployment, simulation-to-real (sim-to-real) transfer, and continuous hardware optimization.
Design, train, and evaluate deep learning, computer vision, and multimodal models (e.g., Vision-Language-Action or VLA systems) for robotic perception, reasoning, and manipulation.
Apply synthetic data, domain randomization, imitation learning, and reinforcement learning within simulation environments before transferring policies to physical robots.
Optimize, quantize, and compile ML models to run with low latency on edge compute hardware, sensors, and embedded robotic platforms.
Build and maintain scalable data ingestion, labeling, automated testing, and MLOps/monitoring pipelines for real-world operational data.
Work side-by-side with mechanical, electrical, controls, and software engineers to integrate AI models into safe, reliable physical machines.
Good to have:
Strong ML fundamentals, computer vision, coding skills, and Claude Code usage
Prior experience on perception models, multimodal datasets, and data pipelines for robotics.
Strong experience in SLAM, computer vision, quick prototyping research papers, model optimization and deployment, Azure ML, claude code.
Good understanding of SLAM, 3D reconstruction, real2sim, human exoskeletons and similar problems.
Preference for hardware or full stack engineering; VLAs or Robotics learning.
Prior 2-4 years of experience in Machine learning at high growth ventures.
Advanced proficiency in Python and familiarity with Linux/Unix development environments.
Practical background in computer vision, sensor fusion (cameras, LiDAR, IMUs), SLAM, or robot control/policies (imitation learning and reinforcement learning).
Experience with edge inference optimization (ONNX, TensorRT), containerization (Docker, Kubernetes), Git, and CI/CD workflows.
Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical/Mechanical Engineering, or a related technical field.

Skills
Machine Learning, Python, Git, Linux, Azure, Docker, Kubernetes, CI/CD, Automation Testing, Deep Learning

Experience
2-4 yrs

Work arrangement
No