Senior Applied Scientist - Ads Ranking & Retrieval

Overview Microsoft Ads powers one of the world’s largest digital advertising ecosystems, delivering billions of recommendations every day. We are seeking a Senior Applied Scientist who is passionate about advancing both frontier research and real-world product impact across our ad retrieval, matching, ranking, and generation systems. In this role, you will drive innovation at the intersection of science and product, developing novel machine learning and AI approaches while ensuring they translate into measurable improvements for customers and the business. You will leverage and advance LLMs, SLMs, LRMs, and other state-of-the-art technologies to solve challenging problems at web scale. Your work will directly influence how ads are discovered, matched, ranked, and generated, improving user experience, advertiser ROI, and the efficiency of the Microsoft Ads platform. We are looking for a scientist who values both deep research and practical execution. You are excited by scientific exploration, but equally motivated by shipping solutions, measuring impact, and iterating based on real-world outcomes. You thrive in collaborative, cross-functional environments and enjoy turning cutting-edge ideas into production systems that serve billions of requests. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees, we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect integrity and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Responsibilities

• Advance research and development across retrieval ranking matching and generative models.
• Leverage and improve SLMs/LLMs:/LRMs train fine-tune and align models and productionize them.
• Evolve the Ads ranking platform toward better usability reliability scalability efficiency and architectural coherence.
• Provide technical leadership on projects set direction coach a distributed team and influence cross-org strategy.
• Follow research trends in AI to guide the group keep solutions state-of-the-art.
• Collaborate with research and engineering teams.

Qualifications Required/Minimum Qualifications:

• Bachelor's Degree in Statistics Econometrics Computer Science Electrical or Computer Engineering or related field AND 4+ 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 3+ years related experience (e.g., statistics predictive analytics research).
• OR Doctorate 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 equivalent experience.

Other Requirements: Ability to meet Microsoft customer and/or government security screening requirements are required for this role These requirements include but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

• Master's Degree in Statistics Econometrics Computer Science Electrical or Computer Engineering or related field AND 5+ years related experience (e.g., statistics predictive analytics research).
• OR Doctorate in Statistics Econometrics Computer Science Electrical or Computer Engineering or related field AND 3+ years related experience (e.g., statistics predictive analytics research).
• OR equivalent experience.
• 6+ years of experience in ML with a proven record of shipping large-scale models to production.
• Expertise in training and inference optimization.
• Proven ability to influence platform architecture and align cross-team roadmaps.
• Track record of publications in tier-1 venues such as NeurIPS ICML KDD WWW ACL SIGIR.
• 5+ years of experience in ML with a proven record of shipping large-scale models to production.
• Hands-on experience with SLM/LLM /LRM training fine-tuning post-training.
• Experience designing scaling recommendation systems with massive query/item spaces multi-stage ranking pipelines.
• Proficiency with deep learning frameworks (e.g., PyTorch Hugging Face TensorFlow) distributed training on large datasets.