Helix AI Engineer, Video Pretraining

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