Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA.
We are looking for a Helix AI Engineer, Robot Learning with a strong robotics learning background to help develop and improve our visuomotor manipulation policies, with a heavy emphasis on real-robot deployment.
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
Bonus Qualifications
- Experience deploying learning-based manipulation systems in commercial or production robotic systems
- Prior work on humanoids or highly dexterous robotic platforms
- Publication record in robot learning, manipulation, or embodied AI
- Experience leading projects or mentoring other engineers
- Passion for building autonomous humanoid robots that operate in the real world
The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.