Apptronik
Principal Robotics Machine Learning Engineer
Austin, TX · Principal
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About the role
- 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.
Responsibilities
- 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.
- 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 entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning models on the robot.
- Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life.
Requirements
- Must be able to lift 15 pounds at times
- AI Model Development Proficiency:
- Expertise in models including CNNs, transformers
- Proficiency in Python, C++, and PyTorch, with experience building real-time robotic software stacks.
- 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.
Nice to have
- 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.
Benefits
- 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.
- Drive the data engine for manipulation learning: data collection strategy, curation, annotation, and synthetic/real data mix, in close partnership with data infrastructure teams.
This listing is sourced directly from Apptronik's careers page and normalized into a canonical job model.