Member of Technical Staff - Large Generative Models

Member of Technical Staff – Large Generative Models

Master’s or PhD in Computer Science, Machine Learning, or related field
Track record of research contributions at top ML conferences (NeurIPS, ICML, ICLR)
Demonstrated experience implementing and improving upon state-of-the-art architectures
Deep expertise in generative modeling approaches (diffusion, autoregressive, VAEs, etc.)
Strong background in optimization techniques and loss function design
Experience with empirical scaling studies and systematic architecture research
Strong proficiency in modern deep learning tooling (PyTorch, CUDA, Triton, FSDP, etc.)
Experience training diffusion models with 10B+ parameters
Deep understanding of attention, transformers, and modern multimodal architectures
Expertise in distributed training systems and model parallelism
Proven ability to implement and improve complex model architectures
Track record of systematic empirical research and rigorous evaluation
Ability to write clean, modular research code that scales
Strong software engineering practices including testing and code review
Experience with rapid prototyping and experimental design
Strong analytical skills for debugging model behavior and training dynamics
Facility with profiling and optimization tools
Track record of bringing research ideas to production
Experience maintaining high code quality in a research environment

Design and implement novel architectures for large-scale video and multimodal diffusion models
Develop new approaches to multimodal fusion, temporal modeling, and video control
Research temporal video editing techniques and controllable generation
Research and validate scaling laws for video generation models
Create new loss functions and training objectives for improved generation quality
Drive rapid experimentation with model architectures and training strategies
Validate research directly through product deployment and user feedback
Train and optimize models at massive scale (10s-100s of billions of parameters)
Develop sophisticated distributed training approaches using FSDP, DeepSpeed, Megatron-LM
Design and implement model surgery techniques (pruning, distillation, quantization)
Create new approaches to memory optimization and training efficiency
Research techniques for improving training stability at scale
Conduct systematic empirical studies of architecture and optimization choices
Advance state-of-the-art in video model architecture design and optimization
Develop new approaches to temporal modeling for video generation
Create novel solutions for multimodal learning and cross-modal alignment
Research and implement new optimization techniques for generative modeling and sampling
Design and validate new evaluation metrics for generation quality
Systematically analyze and improve model behavior across different regimes