Member of Technical Staff - Applied AI Lead, Health

Responsibilities

• Lead, mentor, and grow a team of Applied AI Engineers in a collaborative and high-performing environment.
• Set the technical bar through code and design reviews, remaining a credible authority on LLM systems and evaluations.
• Partner with product leads to co-author the product roadmap and lead the architecture of new products from 0 to 1.
• Define the evaluation strategy by designing systems that test LLM capabilities, accuracy, safety, and utility in the health domain.
• Architect LLM orchestration, guiding the design of agentic, multi-step systems that combine prompt engineering, tool use, and retrieval.
• Direct experiments to determine how different prompting and orchestration techniques affect results on industry benchmarks.
• Invest in internal tooling to improve data pipelines, including dataset sourcing, curation, and synthesis.

Requirements

• Bachelor’s or higher degree in Computer Science or a related technical discipline.
• Significant Python programming experience and/or machine learning research experience.
• Very strong proficiency with LLM evaluations, including designing pipelines and curating datasets.
• Very strong proficiency with LLM orchestration, including agentic systems, tool use, and retrieval.
• 8+ years of software engineering experience.
• At least 3 years of experience leading technical teams or projects as a tech lead or people manager.
• Proven experience delivering high-quality results in fast-paced, 0-to-1 environments.

Preferred Qualifications

• Experience in healthcare technology or the health domain.
• Experience with data engineering and handling text datasets at scale.
• Experience translating cutting-edge research into shipped products.
• Passion for conversational AI and its deployment.
• Strong written and verbal communication skills for cross-functional collaboration.

About the Company

Microsoft AI's Health team is on a mission to help millions of users better understand and proactively manage their health and wellbeing. We turn frontier models into products people can trust with their health by building rigorous, health-specific evaluations and mastering complex LLM orchestration.