Principal Applied Scientist

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Applied Sciences

Principal Applied Scientist role at Microsoft AI Web Data team, focusing on building the data foundation for Bing and Microsoft AI experiences. This involves large-scale grounding, LLM training, and end-to-end processing of web content. The role translates research into production, advancing state-of-the-art modeling and deploying algorithms to improve system performance and accuracy.

What you'd actually do

  1. Develop deep expertise across a broad research area and relevant techniques; stay current on industry trends and advances; and apply these insights to shape product and platform direction.
  2. Partner with stakeholders to understand business and product requirements; incorporate research insights; and provide strategic technical direction for problem solving with solid scientific rigor and measurable business impact.
  3. Mentor and inspire peers and new research talent; build relationships and advocate for research initiatives; share results through industry outreach; collaborate with academia; and strengthen the recruiting pipeline.
  4. Document experiments and outcomes; communicate learnings to accelerate innovation; and help define best practices, including ethics and privacy considerations for research processes and data collection.
  5. Guide and mentor junior team members in developing new technologies that translate into production-ready solutions.

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
  • equivalent 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
  • equivalent 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
  • 8+ years of experience in product development in machine learning and related areas
  • Hands-on experience developing algorithms and models using deep learning frameworks such as TensorFlow and PyTorch
  • Active research in at least one of the following areas: LLM training, artificial intelligence, data science, information retrieval, machine learning, or natural language processing
  • Demonstrated excellence in communication and cross-team collaboration
  • Ability to think big while delivering measurable real-world impact through design and development
  • Solid understanding of web documents and web data processing and understanding concepts, methods, applications, and challenges
  • Experience with Big Data (Spark, Mapreduce, Cosmos, etc.) and NRT systems
  • Experience with Search or recommendations

What the JD emphasized

  • translate research into production
  • advancing the state of the art
  • develop deep expertise
  • shape product and platform direction
  • solid scientific rigor
  • measurable business impact
  • creating publications
  • conducting research
  • developing and deploying live production systems
  • developing and deploying products or systems at multiple points in the product cycle from ideation to shipping
  • product development in machine learning

Other signals

  • LLM training
  • grounding
  • web content processing
  • state-of-the-art modeling
  • translate research into production