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 US base salary range for this full-time position is $200,000 to $400,000 per year.

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.