Helix AI Engineer, Video Pretraining

Figure; San Jose (CA); On-site; USD 100,000 - 140,000; Full time

Job summary

A robotics firm in San Jose seeks a Helix AI Engineer, Video Pretraining, focused on creating large-scale video foundation models trained on diverse data. Responsibilities include designing training models, optimizing performance, and collaborating with teams on advanced AI systems. Required qualifications include experience with video data, proficiency in Python and deep learning frameworks such as PyTorch, and familiarity with distributed training systems. The role demands strong problem-solving skills and independence in research direction. This position requires in-office collaboration five days a week.

Qualifications

• Experience with large-scale pretraining and dataset curation.
• Ability to drive high-impact research directions.
• Experience with large GPU clusters.

Responsibilities

• Design and train large-scale video foundation models.
• Develop pretraining strategies for raw video sequences.
• Optimize model performance and implement efficient data pipelines.
• Collaborate with generative modeling and robot learning teams.

Skills

Experience training large-scale models on video data

Strong understanding of modern deep learning architectures

Proficiency in Python and deep learning frameworks

Experience with distributed training systems

Strong experimental rigor

Solid software engineering skills

Ability to operate independently

Tools

PyTorch

Job description

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.

Our Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are looking for a Helix AI Engineer, Video Pretraining to lead the development of large-scale video foundation models trained on diverse real-world and robot-collected data.

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.

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

Requirements

• 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

Bonus Qualifications

• Experience working on frontier video models or multimodal foundation models
• Background in video diffusion, autoregressive video modeling, or world models
• Experience at leading AI labs such as OpenAI, Google DeepMind, Google, ByteDance, Midjourney, or Adobe
• Experience with large-scale dataset construction and filtering for video pretraining
• Familiarity with robotics, embodied AI, or learning from egocentric / first-person video
• Publication record in machine learning, computer vision, or multimodal AI

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.