Lead Video Foundation Models for Humanoid Autonomy

Figure; Sacramento (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Figure is seeking an experienced Helix AI Engineer to lead video pretraining for autonomous humanoid systems. You will design and train large-scale video models and drive pretraining strategies in collaboration with robotics teams.

Based in San Jose/California, this role requires in-office collaboration five days a week and offers a path to impact embodied AI capabilities through scalable research and production-ready systems.

Qualifications

• Experience training large-scale models on video data or other high-dimensional sequential modalities.
• Strong understanding of modern deep learning architectures for video, vision, or multimodal systems.
• Experience with large-scale pretraining, including dataset curation, training dynamics, and scaling laws.
• Proficiency in Python and deep learning frameworks such as PyTorch.
• Experience working with distributed training systems and large GPU clusters.
• Strong experimental rigor and ability to iterate quickly on model design and training strategies.
• Solid software engineering skills and ability to build scalable, reliable systems.
• Ability to operate independently and drive ambiguous, high-impact research directions.

Responsibilities

• Design and train large-scale video foundation models on diverse datasets spanning internet-scale video and robot-collected data
• Develop pretraining strategies that capture temporal dynamics, motion, and object interaction from raw video sequences
• Build models that learn transferable representations for downstream tasks such as perception, tracking, prediction, and control
• Explore architectures for video understanding and generation, including transformer-based and diffusion-based approaches
• Implement efficient data pipelines and training strategies for high-throughput video ingestion and large-scale distributed training
• Optimize model performance across compute, memory, and training efficiency constraints
• Collaborate closely with generative modeling, agent, and robot learning teams to integrate pretrained models into the autonomy stack
• Design evaluation frameworks and benchmarks to measure temporal understanding, prediction quality, and generalization

Skills

Python programming

Deep learning

Video understanding

Experimentation

Independent research

Tools

PyTorch

Distributed training frameworks

Job description

Figure is seeking an experienced Helix AI Engineer to lead video pretraining for autonomous humanoid systems. You will design and train large-scale video models and drive pretraining strategies in collaboration with robotics teams.

Based in San Jose/California, this role requires in-office collaboration five days a week and offers a path to impact embodied AI capabilities through scalable research and production-ready systems.