Senior Applied Scientist (bing Places)

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

Senior Applied Scientist on the Bing Places team, responsible for designing, building, and shipping advanced AI and ML solutions including LLMs, RAG, learning-to-ranking, and entity understanding for local search experiences. The role involves end-to-end ownership from problem formulation and data analysis to model development, experimentation, production deployment, and live flighting, collaborating with engineering and product partners.

What you'd actually do

  1. Formulate complex product and engineering problems as machine learning and AI tasks, and drive them from concept through production.
  2. Design, implement, and evaluate ML‑ and LLM‑based models that improve Bing Places quality, relevance, and coverage.
  3. Conduct rigorous data analysis to understand system behavior, identify opportunities, and define success metrics.
  4. Prototype new modeling approaches and iterate quickly based on offline evaluation and online experimentation.
  5. Own experimentation pipelines, including offline validation and large‑scale online A/B flighting.

Skills

Required

  • Bachelor's Degree in Computer Science, or Computer Engineering, or related field AND 4+ years related experience OR Master's Degree in Computer Science, or Computer Engineering, or related field AND 3+ years related experience OR Doctorate in Statistics, Econometrics, Computer Science, or Computer Engineering, or related field AND 1+ year(s) related experience OR equivalent experience.

Nice to have

  • Doctorate in Computer Science, or Computer Engineering, or related field AND 3+ years related experience
  • 3+ years of experience applying AI solutions or LLMs to real‑world systems (RAG, ranking, classification, reasoning).
  • Proven experience in distributed training, model optimization, and production ML infrastructure.
  • Hands‑on experience developing and evaluating models on large‑scale, real‑world datasets.
  • Proficiency in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX, or similar).
  • In depth nderstanding of experimentation methodologies, including offline metrics and online A/B testing.
  • Ability to independently scope problems and deliver high‑quality solutions in ambiguous environments.
  • Proven collaboration skills and experience working with engineering and product partners.
  • Ability to clearly communicate technical concepts and trade‑offs to both technical and non‑technical audiences.
  • Comfort operating across the full lifecycle—from research and prototyping to production and live operations.

What the JD emphasized

  • advanced AI and machine learning solutions
  • large language models (LLMs)
  • retrieval augmented generation (RAG)
  • learning‑to‑ranking
  • entity understanding
  • high‑quality, trustworthy local search experiences at scale
  • deep technical expertise
  • real‑world impact
  • end‑to‑end
  • production deployment
  • live flighting
  • large‑scale real‑world datasets
  • distributed training
  • model optimization
  • production ML infrastructure
  • large‑scale online A/B flighting

Other signals

  • LLMs
  • RAG
  • learning-to-ranking
  • entity understanding
  • production deployment
  • large-scale online A/B flighting