Helix AI Engineer, Robot Learning

Figure; San Jose (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

A leading AI robotics company is seeking a Helix AI Engineer specializing in robot learning to design and improve visuomotor manipulation policies. Key responsibilities include developing manipulation behaviors and integrating policies into a full autonomy stack. Candidates should have hands-on experience with robot learning systems and strong skills in robot manipulation and control. This role offers the opportunity to work on innovative technologies for autonomous humanoid robots in a dynamic environment.

Qualifications

• Hands-on experience developing and deploying robot learning systems on real robots.
• Strong background in robot manipulation and visuomotor control.
• Experience with behavior cloning or reinforcement learning.

Responsibilities

• Design, train, evaluate, and deploy learning-based visuomotor policies.
• Develop manipulation behaviors such as grasping and pick-and-place.
• Collaborate with other teams to integrate policies into a full autonomy stack.

Skills

Robot manipulation

Visuomotor control

Behavior cloning

Reinforcement learning

Python

C++

Tools

PyTorch

Simulation tools

Real robot deployment

Job description

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.