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, Pretraining</strong> to build large-scale foundation models that learn from diverse data sources including text, images, video, and robot-collected experience.</p> <p>This role focuses on advancing pretraining methods that enable generalization, reasoning, and adaptability—forming the backbone for downstream capabilities in perception, planning, and action.</p> <h2><strong>Responsibilities</strong></h2> <ul> <li>Design and train large-scale foundation models across multimodal data (e.g., text, vision, and robot data)</li> <li>Develop pretraining strategies that improve generalization, reasoning, and transfer to downstream embodied tasks</li> <li>Explore and implement architectures including transformer-based and emerging foundation model paradigms</li> <li>Work on scaling laws, dataset mixture design, and training dynamics for frontier models</li> <li>Build and optimize large-scale distributed training pipelines across multi-node GPU clusters</li> <li>Collaborate closely with video, generative, agent, and robot learning teams to integrate pretrained models into the autonomy stack</li> <li>Design evaluation frameworks to measure reasoning ability, robustness, and cross-domain generalization</li> <li>Contribute to post-training approaches including fine-tuning, alignment, and model adaptation</li> </ul> <h2><strong>Requirements</strong></h2> <ul> <li>Experience training large-scale foundation models or working on pretraining for LLMs or multimodal systems</li> <li>Strong understanding of modern deep learning architectures, especially transformers</li> <li>Experience with large-scale distributed training and optimization</li> <li>Proficiency in Python and deep learning frameworks such as PyTorch</li> <li>Strong experimental rigor and ability to iterate on model design and training strategies</li> <li>Solid software engineering skills and ability to build scalable, reliable systems</li> <li>Ability to operate independently and drive ambiguous, high-impact technical problems</li> </ul> <h2><strong>Bonus Qualifications</strong></h2> <ul> <li>Experience working on frontier foundation models at companies such as Anthropic, OpenAI, Google DeepMind, or xAI</li> <li>Experience with multimodal pretraining (vision-language or vision-language-action models)</li> <li>Background in scaling laws, dataset curation, and large-scale data mixture optimization</li> <li>Experience with post-training techniques such as RLHF, reward modeling, or alignment methods</li> <li>Familiarity with embodied AI, robotics, or real-world deployment constraints</li> <li>Publication record in machine learning, NLP, or multimodal AI</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>