Applied Scientist II (Bing Places)

Sorry, this job was removed at 08:48 a.m. (UTC) on Monday, Jul 27, 2026
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2 Locations
In-Office
102K-219K Annually
Junior
Software • Quantum Computing • Metaverse • Infrastructure as a Service (IaaS)
The Role
Design, develop, and deploy ML and LLM-based models (RAG, ranking, entity understanding) for Bing Places. Perform data analysis, prototype models, run offline and large-scale online experiments, and partner with engineering to integrate models into production.
Summary Generated by Built In
Overview

The Bing Places team is building intelligence that powers local search experiences used by millions of people every day. We are looking for Applied Scientists to help design, build, and ship advanced AI and machine learning solutions—spanning large language models (LLMs), retrieval augmented generation (RAG), learning‑to‑ranking, and entity understanding—to deliver high‑quality, trustworthy local search experiences at scale.

As an Applied Scientist on Bing Places,

  • You will work on challenging problems that require deep technical expertise and a strong focus on real‑world impact.
  • You will work end‑to‑end: from problem formulation and data analysis, through model development and experimentation, to production deployment and live flighting.
  • You will collaborate closely with engineering and product partners to develop, experiment with, and ship models that operate at Microsoft scale, while contributing to the broader scientific community through publications and patents

Responsibilities
  • Formulate complex product and engineering problems as machine learning and AI tasks, and drive them from concept through production
  • Design, implement, and evaluate ML‑ and LLM‑based models that improve Bing Places quality, relevance, and coverage
  • Conduct rigorous data analysis to understand system behavior, identify opportunities, and define success metrics
  • Prototype new modeling approaches and iterate quickly based on offline evaluation and online experimentation
  • Own experimentation pipelines, including offline validation and large‑scale online A/B flighting
  • Partner closely with engineers to integrate models into production systems and ensure long‑term reliability and performance
  • Drive technical direction within your problem space and influence broader modeling and platform decisions
  • Document and communicate results through technical design reviews, papers, and patent filings

Qualifications
Required Qualifications:
  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research)
    • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
    • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience.

Preferred Qualifications: 

  • Master’s degree or PhD in a relevant technical field
  • 4+ years of experience applying AI solutions or LLMs to real‑world systems (RAG, ranking, classification, reasoning)
  • Proven expertise in machine learning, statistical methods, and data‑driven problem solving
  • 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)
  • Understanding of experimentation methodologies, including offline metrics and online A/B testing
  • Ability to independently scope problems and deliver high‑quality solutions in ambiguous environments
  • Strong 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

  • Background in search, information retrieval, knowledge graphs, or local/entity understanding
  • Track record of publications or granted/pending patents
  • Familiarity with distributed training, model optimization, and production ML infrastructure
  • Comfort operating across the full lifecycle—from research and prototyping to production and live operations

Applied Sciences IC3 - The typical base pay range for this role across the U.S. is USD $102,100 - $202,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $133,800 - $219,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay


This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.



Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Skills Required

  • Bachelor's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience OR Master's degree AND 1+ year related experience OR Doctorate or equivalent experience.
  • Master's degree or PhD in a relevant technical field
  • 4+ years of experience applying AI solutions or LLMs to real-world systems (RAG, ranking, classification, reasoning)
  • Proven expertise in machine learning, statistical methods, and data-driven problem solving
  • 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)
  • Understanding of experimentation methodologies, including offline metrics and online A/B testing
  • Background in search, information retrieval, knowledge graphs, or local/entity understanding
  • Track record of publications or granted/pending patents
  • Familiarity with distributed training, model optimization, and production ML infrastructure
  • Ability to operate across research, prototyping, production, and live operations
  • Strong collaboration and communication skills with engineering and product partners

Microsoft Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Microsoft and has not been reviewed or approved by Microsoft.

  • Fair & Transparent Compensation Pay is presented as broadly competitive overall, with clear role/level/location variation and an emphasis on using posted ranges and band information for apples-to-apples comparisons.
  • Retirement Support Retirement benefits are described as a standout, highlighted by a strong 401(k) match structure and immediate vesting, plus additional plan features for tax-advantaged saving.
  • Parental & Family Support Family-oriented benefits are portrayed as a meaningful strength, with substantial paid parental leave and added supports like back-up care and adoption/surrogacy assistance.

Microsoft Insights

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