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