Applied Scientist II (Bing Places)

Microsoft•Redmond, WA

2d•$100,600 - $199,000

About The Position

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 Bing Location Understanding and Geocoding team - (Redmond, WA) The Bing Location Understanding (BLU) and Bing Geocoding (BingGC) teams build the core intelligence that powers location interpretation, address understanding, and geospatial reasoning across Bing, Maps, and downstream Microsoft experiences. Our systems operate at global scale and combine machine learning, natural language understanding, ranking, and large‑scale data processing to deliver high‑quality results in real time.

Requirements

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.

Nice To Haves

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

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

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