Robotics Machine Learning Engineer

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond.

We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.

JOB SUMMARY

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. 

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

• 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.

SKILLS AND REQUIREMENTS

Technical Skills (Must-Have)

• Deployment Experience: 5+ years shipping machine learning models on robotic systems in production environments.