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Helix AI Engineer, Robot Learning
Figure
Napi Headquarters, USA
Mid
USD 200000-400000 / year
Discovered 1 weeks ago
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Responsibilities
- Design, train, evaluate, and deploy learning-based visuomotor policies for humanoid robot manipulation
- Develop manipulation behaviors such as grasping, pick-and-place, object reorientation, door opening, bimanual manipulation, and basic assembly
- Apply and extend techniques including behavior cloning, reinforcement learning, and VLA reasoning
- Train models that are robust to real-world challenges such as sensor noise, partial observability, contact dynamics, and environment variability
- Own the full pipeline from data collection on real robots to model training, evaluation, and deployment
- Work closely with simulation and digital twin tooling where useful, while prioritizing real-world performance and transfer
- Collaborate with perception, controls, systems, and hardware teams to integrate policies into a full autonomy stack
- Evaluate tradeoffs between learning-based and classical approaches and make principled design decisions
- Write high-quality, well-tested software that ships to and runs reliably on physical humanoid robots
- Partner with integration and testing teams to continuously improve robustness, performance, and deployment velocity
Requirements
- Hands-on experience developing and deploying robot learning systems on real robots
- Strong background in robot manipulation and visuomotor control
- Experience with behavior cloning, reinforcement learning, or related learning-based manipulation methods
- Proficiency in Python and/or C++ for robotics and ML systems
- Experience with modern deep learning frameworks (e.g., PyTorch)
- Ability to design experiments, analyze failures, and iterate quickly in real-world robotic systems
- Solid understanding of the tradeoffs between classical robotics approaches and learning-based methods
- Thrive in fast-paced, ambiguous environments where solutions require exploration and ownership
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