Member of Technical Staff - Applied AI Lead, Health

Microsoft Microsoft · Big Tech · London, United Kingdom +1 · Software Engineering

Applied AI Lead for Microsoft's Health team, focusing on building Copilot Health. This hands-on leadership role involves setting technical direction for LLM evaluations and orchestration, mentoring engineers, and bridging research with product delivery in the health domain. The role requires strong proficiency in designing and running LLM evaluations, building agentic multi-step systems, and ensuring the safety and utility of AI models in healthcare.

What you'd actually do

  1. Lead, mentor and grow a team of Applied AI Engineers, fostering a collaborative, inclusive and high-performing environment where engineers do the best work of their careers.
  2. Stay deeply hands-on. Set the technical bar through code and design reviews, lead by example on the hardest problems, and remain a credible technical authority on evals and LLM systems.
  3. Co-own the roadmap. Partner with product leads to qualify and size new opportunities, co-author the product roadmap, and lead the architecture and development of new products and features from 0 to 1.
  4. Define the evaluation strategy. Design and oversee evaluation systems that test LLM capabilities in the health domain, including internal benchmarking and regression testing that capture model accuracy, safety and utility - and make sure results are interpreted and clearly communicated to stakeholders.
  5. Architect LLM orchestration. Guide the design of agentic, multi-step systems that combine prompt / context engineering, tool use and retrieval, and champion best practices for building and deploying them reliably at scale.

Skills

Required

  • Python programming experience
  • machine learning research
  • LLM evaluations
  • LLM orchestration
  • prompt / context engineering
  • tool use
  • harness engineering
  • retrieval
  • agentic, multi-step systems
  • engineering leadership
  • mentoring and developing engineers
  • shipping and learning
  • collaborating in cross-functional teams
  • working through ambiguity

Nice to have

  • healthcare technology experience
  • health domain experience
  • data engineering
  • translating cutting-edge research into shipped products
  • conversational AI
  • written and verbal communication skills
  • learning new technologies
  • staying up to date with industry trends

What the JD emphasized

  • health-specific evals
  • LLM evaluations
  • LLM orchestration
  • agentic, multi-step systems
  • tool use
  • retrieval
  • 0-to-1 experience

Other signals

  • building agentic, multi-step systems
  • LLM evaluations
  • health-specific evals
  • orchestration
  • tool use
  • retrieval