Principal Robotics Machine Learning Engineer

The Principal Manipulation Engineer is a core contributor to our robot’s ability to interact with the world with human-like world model context, dynamic perception, and interactive behavior. This role is responsible for the end-to-end lifecycle of learned manipulation, from data engine design through training to on-robot deployment and is expected to diagnose performance bottlenecks. This requires deep fluency in both classical computer vision and modern deep learning to unlock the full potential of state-of-the-art humanoid robot hardware.

This role will bridge the gap between cutting-edge research and scalable, reliable production software. We move at startup pace; intense and focused, but sustainable. We operate with high ownership, fast feedback, and low bureaucracy. 

• →Implement and deploy state-of-the-art ML models/algorithms to achieve ambitious, world-class performance on open world object manipulation tasks with physical hardware.
• →Drive the data engine for manipulation learning: data collection strategy, curation, annotation, and synthetic/real data mix, in close partnership with data infrastructure teams.
• →Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning models on the robot.
• →Identify and prioritize performance bottlenecks, distinguishing between root causes traceable to classical vision (calibration, geometry, sensor fusion) vs those rooted in sub-optimal model/data, to prioritize fixes according to customer-expected levels of reliability.
• →Act as a force multiplier across the organization. Beyond code reviews, foster a culture of technical rigor, setting the bar for architectural excellence and mentoring the next generation of robotics leaders.

Prolonged periods of sitting at a desk and working on a computer
Must be able to lift 15 pounds at times
Vision to read printed materials and a computer screen
Hearing and speech to communicate

• Deployment Experience: 5+ years shipping machine learning models on robotic systems in production environments.
• AI Model Development Proficiency: Expertise in models including CNNs, transformers, and VLAs.
• Software Engineering: Proficiency in Python, C++, and PyTorch, with experience building real-time robotic software stacks.
• AI Data Pipeline Experience: Experience with data collection, annotation, and synthetic data generation for ML.
• Computer Vision: Familiarity with 6D pose estimation, camera calibration/extrinsics, point cloud processing and visual-servoing.
• Hardware Bring-up: Experience with the initial calibration and tuning of high-DOF robotic manipulators.

• ML Ops: Familiarity with distributed training and MLOps principles.
• Teleoperation: Experience with VR/haptic interfaces and retargeting algorithms for human-in-the-loop control.
• Simulation Environments: Experience with physics engines such as IsaacSim, MuJoCo, or Drake for policy training and validation.
• Logistics Automation Background: Experience using manipulators to automate dynamic real-world material handling tasks.

• BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
• 7+ years of relevant experience (or 5+ years with a PhD) specifically focused on robotic manipulation or complex motion control.
• A proven track record of taking complex algorithms from conception to successfully deploying them on physical hardware.