About the job
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.
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
Minimum
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
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