Helix AI Engineer, Video Pretraining
$200k - $400k • San Jose, CA
Posted 9d ago
Job Location
San Jose, CA
Tech Stack
Remote Work Policy
On-site
Categories
Machine Learning Engineer
About the job
Figure is an AI robotics company developing autonomous general-purpose humanoid robots with the goal of shipping robots with human-level intelligence for home and commercial markets. The Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are seeking 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.
- Develop pretraining strategies to capture temporal dynamics, motion, and object interaction from raw video.
- Build models that learn transferable representations for downstream tasks like 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 with generative modeling, agent, and robot learning teams to integrate pretrained models.
- 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.
Benefits
- The US base salary range for this full-time position is $200,000 to $400,000 per year.
- The total compensation package may also include additional components/benefits depending on the specific role.