Helix AI Engineer, Video Pretraining

<div class="content-intro"><p class="p1">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.</p></div><p>Our Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are looking for a&nbsp;<strong>Helix AI Engineer, Video Pretraining</strong> to lead the development of large-scale video foundation models trained on diverse real-world and robot-collected data.</p> <p>This role focuses on pretraining models that learn from raw video—capturing motion, interaction, and temporal structure—to enable downstream capabilities in perception, prediction, and embodied reasoning.</p> <p><strong>Responsibilities:</strong></p> <ul> <li> <p>Design and train large-scale video foundation models on diverse datasets spanning internet-scale video and robot-collected data.</p> </li> <li> <p>Develop pretraining strategies that capture temporal dynamics, motion, and object interaction from raw video sequences.</p> </li> <li> <p>Build models that learn transferable representations for downstream tasks such as perception, tracking, prediction, and control.</p> </li> <li> <p>Explore architectures for video understanding and generation, including transformer-based and diffusion-based approaches.</p> </li> <li> <p>Implement efficient data pipelines and training strategies for high-throughput video ingestion and large-scale distributed training.</p> </li> <li> <p>Optimize model performance across compute, memory, and training efficiency constraints.</p> </li> <li> <p>Collaborate closely with generative modeling, agent, and robot learning teams to integrate pretrained models into the autonomy stack.</p> </li> <li> <p>Design evaluation frameworks and benchmarks to measure temporal understanding, prediction quality, and generalization.</p> </li> </ul> <p><strong>Requirements:</strong></p> <ul> <li> <p>Experience training large-scale models on video data or other high-dimensional sequential modalities.</p> </li> <li> <p>Strong understanding of modern deep learning architectures for video, vision, or multimodal systems.</p> </li> <li> <p>Experience with large-scale pretraining, including dataset curation, training dynamics, and scaling laws.</p> </li> <li> <p>Proficiency in Python and deep learning frameworks such as PyTorch.</p> </li> <li> <p>Experience working with distributed training systems and large GPU clusters.</p> </li> <li> <p>Strong experimental rigor and ability to iterate quickly on model design and training strategies.</p> </li> <li> <p>Solid software engineering skills and ability to build scalable, reliable systems.</p> </li> <li> <p>Ability to operate independently and drive ambiguous, high-impact research directions.</p> </li> </ul> <p><strong>Bonus Qualifications:</strong></p> <ul> <li> <p>Experience working on frontier video models or multimodal foundation models.</p> </li> <li> <p>Background in video diffusion, autoregressive video modeling, or world models.</p> </li> <li> <p>Experience at leading AI labs such as OpenAI, Google DeepMind, Google, ByteDance, Midjourney, or Adobe.</p> </li> <li> <p>Experience with large-scale dataset construction and filtering for video pretraining.</p> </li> <li> <p>Familiarity with robotics, embodied AI, or learning from egocentric / first-person video.</p> </li> <li> <p>Publication record in machine learning, computer vision, or multimodal AI.</p> </li> </ul> <p class="p1">The US base salary range for this full-time position is $200,000 to $400,000 per year.</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">&nbsp;</span></p>