Research Engineer - World Modeling

Team: Engineering

Location: Sunnyvale, CA

Commitment: Full-time

Workplace Type: onsite

Key Responsibilities: Data Infrastructure & Pipelines

• Design, implement, and maintain scalable video data pipelines to support large-scale training. 
• Develop data preprocessing, transformation, and synthesis workflows to support world model training. 
• Contribute to building high-quality data annotation pipelines to ensure accurate and consistent labels across large-scale datasets. 

Key Responsibilities: Training & Inference Systems

• Support the training of multimodal foundation models (e.g., video diffusion models, world models) by developing and optimizing distributed training systems. 
• Improve inference and serving efficiency for real-time interaction through model optimization and system tuning. 
• Monitor system health and performance, and contribute to debugging and optimization at scale. 

Key Responsibilities: Collaboration & Integration

• Work closely with research teams to understand experimental goals and translate ideas into reliable and maintainable infrastructure and tools. 
• Integrate novel research prototypes into production-ready systems and ensure reproducibility at scale. 
• Participate in design and code reviews, ensuring code quality, efficiency, and compliance with best practices. 

Key Responsibilities: Benchmarking & Evaluation

• Contribute to the development of tools and infrastructure to evaluate model performance using rigorous quantitative benchmarks, including metrics for physical accuracy and controllability. 

Key Responsibilities: Codebase & Documentation

• Maintain and extend shared codebases, contribute to internal documentation, and support onboarding of new team members or collaborators. 
• Write clean, efficient, and well-tested code for components across the model development lifecycle. 

Key Responsibilities

• Support contributions to research papers and demos when engineering work plays a significant role. 
• Help represent the team’s engineering excellence in internal and external forums when appropriate. 

Academic Qualifications

• MSc or PhD in Machine Learning or Computer Science, or equivalent industry experience. 

Professional Experience Required

• Proficient in data collection, cleaning, and transformation at scale, including designing robust pipelines for multimodal datasets (e.g., video, audio, text). 
• Practical experience with web scraping and crawling frameworks (e.g., scrapy, selenium, playwright, BeautifulSoup) to collect and curate high-quality web-scale datasets. 
• Experience in large-scale model training (LLMs or Diffusion Models) on large clusters. 
• Hands-on experience with state-of-the-art video generative models (e.g., Sora, Veo2, MovieGen, CogVideoX, etc.). 
• Experiences in building and optimizing large-scale video data pipelines. 
• Experience in accelerating diffusion model inference for improved efficiency. 
• Exceptional problem-solving and troubleshooting skills to tackle complex technical challenges. 
• Strong systems and engineering expertise in deep learning frameworks such as PyTorch. 
• Strong communication and collaboration skills for effective cross-functional teamwork. 
• Demonstrated ability to solve complex system-level challenges and debug failures across the training/inference stack (e.g., memory issues, deadlocks, I/O bottlenecks).