Figure is a Bay Area robotics startup building AI-powered humanoid robots for industrial tasks such as warehouse and factory work.
<p>Figure is an AI robotics company developing autonomous general-purpose humanoid robots. Our goal is to build embodied AI systems that can perceive, reason, and act in the real world. Figure is headquartered in San Jose, CA, and this role requires 5 days/week in-office collaboration.</p> <p>Our Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are looking for a <strong>Helix AI Engineer, Generative AI</strong> to build and scale generative models that enable robots to understand, simulate, and interact with the physical world. This role focuses on training and deploying diffusion and generative models across vision, video, and multimodal domains, with applications spanning perception, data generation, and model-based reasoning.</p> <h2><strong>Responsibilities</strong></h2> <ul> <li>Design, train, and deploy large-scale generative models, with a focus on diffusion-based approaches for vision, video, and multimodal data</li> <li>Develop models that improve robot perception, world modeling, and prediction from raw sensory inputs</li> <li>Build generative systems for synthetic data creation, augmentation, and dataset scaling for robot learning</li> <li>Explore and implement state-of-the-art techniques in diffusion, generative modeling, and multimodal foundation models</li> <li>Optimize training pipelines for large-scale generative models across distributed systems</li> <li>Work closely with data, training infrastructure, and agent teams to integrate generative models into the full autonomy stack</li> <li>Evaluate model quality, robustness, and generalization across real-world scenarios</li> <li>Contribute to the design of scalable experimentation frameworks for generative model development</li> </ul> <h2><strong>Requirements</strong></h2> <ul> <li>Experience training and deploying generative models (diffusion, autoregressive, or related approaches) at scale</li> <li>Strong understanding of modern deep learning techniques for vision and/or multimodal systems</li> <li>Proficiency in Python and deep learning frameworks such as PyTorch</li> <li>Experience working with large-scale datasets and distributed training systems</li> <li>Strong experimental rigor and ability to iterate quickly on model performance</li> <li>Solid software engineering skills and ability to build reliable, maintainable systems</li> <li>Ability to operate independently and own ambiguous, high-impact technical problems</li> </ul> <h2><strong>Bonus Qualifications</strong></h2> <ul> <li>Experience with diffusion models for image or video generation</li> <li>Experience with multimodal foundation models (vision-language or vision-language-action)</li> <li>Background in synthetic data generation or simulation for robotics or embodied AI</li> <li>Experience optimizing large-scale training (multi-node, GPU clusters, etc.)</li> <li>Familiarity with 3D, video prediction, or world models</li> <li>Prior work in robotics, embodied AI, or real-world ML systems</li> <li>Publication record in machine learning, computer vision, or generative modeling</li> </ul> <p>The US <strong>base</strong> salary range for this full-time position is between $200,000 - $400,000</p> <p>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.<span class="Apple-converted-space"> </span></p>