Helix AI Engineer, Robot Learning

Figure•San Jose, CA

About The Position

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.

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

Nice To Haves

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

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