Principal Applied Scientist

Microsoft Microsoft · Big Tech · Zürich, ZH, Switzerland · Applied Sciences

This role focuses on designing, building, and deploying cloud-based AI applications, specifically LLM-based agentic systems, for enterprise use within M365 Copilot. The goal is to fine-tune LLMs on tenant data to create task-specific agents and solutions, advancing the state of the art in enterprise AI.

What you'd actually do

  1. Design, build, test, and deploy cloud-based AI applications.
  2. Build and deploy LLM-based agentic systems.
  3. Engage with customers to understand and address their pain points.
  4. Document experiments and communicate results across the team.
  5. Mentor early in career team members.

Skills

Required

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience
  • Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience
  • Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience

Nice to have

  • Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience
  • Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience
  • 5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
  • 2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
  • 5+ years experience conducting research as part of a research program (in academic or industry settings).
  • 3+ years experience developing and deploying live production systems, as part of a product team.
  • 3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

What the JD emphasized

  • fine-tune large language models (LLMs) on tenant data
  • task-specific agents
  • advancing the state of the art of models in M365 Copilot
  • designing, building and deploying AI products
  • working with the latest cloud and ML technology
  • generalize the learnings for the broader product
  • Build and deploy LLM-based agentic systems

Other signals

  • fine-tune large language models (LLMs) on tenant data
  • task-specific agents
  • advancing the state of the art of models in M365 Copilot
  • designing, building and deploying AI products
  • working with the latest cloud and ML technology
  • generalize the learnings for the broader product
  • Build and deploy LLM-based agentic systems