Research Engineer ML redwood

Research Engineer on the ML redwood city

Research Engineer on the ML Systems team, you'll be working on cutting-edge ML training and inference systems, optimizing the performance and efficiency of our GPU clusters, and developing new technologies that fine-tune leading consumer AI models with a data flywheel, and serve 20K+ QPS in production with LLMs. Your work will directly contribute to our groundbreaking advancements in AI, helping shape an era where technology is not just a tool, but a companion in our daily lives.

, your talent, creativity, and expertise will not just be valued-they will be the catalyst for change in an AI-driven future.

The ML Systems team is responsible for the research and deployment of systems that efficiently utilize GPU for AI-enabled products.

As a research engineer, you will work across teams and our technical stack to improve our training performance and inference runtime. You will get to shape the conversational experience of millions of users per day.

• Write efficient Triton kernels and tune them for our specific models and hardware
• Develop prefix-aware routing algorithms to improve serving cache hit rate
• Train and distill LLMs to improve latency while preserving accuracy and engagements
• Build an efficient and scalable distributed RLHF stack powering the model innovations
• Develop systems for efficient multimodal (image gen/video gen) model training & inference

• “All Industry Levels”: at least PhD (or equivalent) research experience
• Write clear and clean production system code
• Strong understanding of modern machine learning techniques (reinforcement learning, transformers, etc)
• Track record of exceptional research or creative ML systems projects
• Comfortable writing model development code (PyTorch) for either training or inference

• Experience training large models in a distributed setting utilizing PyTorch distributed, Deep Speed, Megatron.
• Experience working with GPUs & collectives (training, serving, debugging) and writing kernels (Triton, CUDA, CUTLASS).
• Experience with LLM inference systems and literature such as vLLM and Flash Attention.
• Familiarity with ML deployment and orchestration (Kubernetes, Docker, cloud)
• Publications in relevant academic journals or conferences in the field of machine learning and systems