Research Engineer; Model Efficiency

Requirements

• Have a PhD in Machine Learning or a related field
• Understand LLM architecture, and how to optimize LLM inference given resource constraints
• Have significant experience with one or more techniques that enhance model efficiency
• Strong software engineering skills
• An appetite to work in a fast-paced high-ambiguity start-up environment
• Publications at top-tier conferences and venues (ICLR, ACL, NeurIPS)
• Passion to mentor others
• If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply!

• Large Language Models (LLMs) continue to push the boundaries of what AI systems can do - but inference is still the bottleneck
• The Model Efficiency team is responsible for pushing the limits of LLM inference efficiency across our foundation models. We explore and ship breakthroughs across the model execution stack, including:
• Model architecture and MoE routing optimization
• Decoding and inference-time algorithm improvements
• Software/hardware co-design for GPU acceleration
• Performance optimization without compromising model quality
• As a Staff Research Engineer, you will develop, prototype, and deploy techniques that materially improve how fast and efficiently our models run in production