Helix AI Engineer, Robot Learning
Figure · Not provided by source
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Job details
- Work model
- Not provided by source
- Salary
- Not listed by source
- Location
- San Jose, CA
What the role asks for
What you'd do
- 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
What they're asking for
- Hands-on experience developing and deploying robot learning systems on real robotsSkill
- Strong background in robot manipulation and visuomotor controlSkill
- Experience with behavior cloning, reinforcement learning, or related learning-based manipulation methodsSkill
- Proficiency in Python and/or C++ for robotics and ML systemsSkill
- Experience with modern deep learning frameworks (e.g., PyTorch)Skill
- Ability to design experiments, analyze failures, and iterate quickly in real-world robotic systemsSkill
- Solid understanding of the tradeoffs between classical robotics approaches and learning-based methodsSkill
- Thrive in fast-paced, ambiguous environments where solutions require exploration and ownershipSkill
- Experience deploying learning-based manipulation systems in commercial or production robotic systemsSkillPreferred
- Prior work on humanoids or highly dexterous robotic platformsSkillPreferred
- Publication record in robot learning, manipulation, or embodied AISkillPreferred
- Experience leading projects or mentoring other engineersSkillPreferred
- Passion for building autonomous humanoid robots that operate in the real worldSkillPreferred
Parsed by MeritLog from the employer’s own posting. The full description follows below.
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 US base salary range for this full-time position is between $200,000 - $400,000. 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.