Mitchell Naylor

I work as a technical leader in GitHub Copilot's Applied Science team, where I lead small teams of applied researchers to execute on high-impact, cross-functional efforts to improve Copilot products. This has included:

• Post-training LLMs to improve model performance on high-priority applications
• Leading efforts to use reinforcement learning (RL) for agentic tasks, including designing reward functions, training pipelines, and evaluation processes
• Partnering with engineering and product teams to develop and improve early-stage products
• Designing agentic systems to accomplish complex software engineering tasks, such as reviewing pull requests (see Copilot Code Review)
• Mentoring junior team members through pairing and sharing knowledge of ML best practices
• Conducting A/B tests to quantify model improvements, and contributing to a culture of shipping quickly and rigorously
• Acting as a key member of the hiring team for applied researchers, including designing technical interview material and assessing prospective team members

At Azra AI, I operated as a "full-stack" data scientist, from collaborating with clinical stakeholders to leading research efforts on efficient attention mechanisms for Transformers and model pretraining.

• Led research on efficient architectures for long-context NLP, implementing and adapting emerging methods (including MEGA architecture contribution to Hugging Face Transformers) for clinical NLP
• Designed pretraining and domain-adaptation pipelines, producing language models optimized for deployment on resource-constrained hardware
• Built and deployed clinical NLP models (NER, classification) identifying high-risk findings for 200k+ patients annually, enabling early detection and faster treatment
• Drove technical strategy for ML product development, evaluating cutting-edge research and rapidly prototyping novel architectures for production deployment
• Collaborated with clinical stakeholders to ensure model accuracy and reliability met the high standards required for cancer detection workflows

Note: Azra AI spun out as a standalone company in January 2022, previously part of Digital Reasoning

• Applied methods from NLP, statistical inference, and operations research to solve a diverse set of business problems
• Communicated findings to stakeholders ranging from highly technical to C-level

• Built predictive models for customer behavior under various pricing scenarios
• Conducted robust statistical analyses in support of pricing department