Senior Applied Scientist - Ads Ranking & Retrieval

Microsoft Ads seeks a Senior Applied Scientist to advance ad retrieval, matching, ranking, and generation at web scale. The role combines research with production delivery, aiming to improve user experience, advertiser return on investment, and platform efficiency. It is based in Bangalore, Karnataka, India, with four days per week in the office and less than 25% travel.

Responsibilities

• Research and develop retrieval, ranking, matching, and generative models. Train, fine-tune, align, improve, and productionize small language models, large language models, and large reasoning models.
• Improve the Ads ranking platform’s usability, reliability, scalability, efficiency, and architectural coherence. Ship solutions, measure their impact, and iterate on real-world outcomes.
• Set technical direction, coach a distributed team, influence cross-organization strategy, follow AI research trends, and collaborate with research and engineering teams.

Required qualifications

• A bachelor’s degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field with 4+ years of related experience; a master’s degree in one of these fields with 3+ years; a doctorate with 1+ year; or equivalent experience. Related experience includes statistics, predictive analytics, or research.
• Ability to meet applicable Microsoft, customer, or government security-screening requirements. The Microsoft Cloud background check is required upon hire or transfer and every two years thereafter.

Preferred qualifications

• A relevant master’s degree with 5+ years of related experience, a doctorate with 3+ years, or equivalent experience. The posting separately cites 6+ and 5+ years of machine-learning experience shipping large-scale models to production.
• Expertise in training and inference optimization; experience with SLM, LLM, and LRM training, fine-tuning, and post-training; and experience scaling recommendation systems with massive query and item spaces and multi-stage ranking pipelines.
• Proficiency with PyTorch, Hugging Face, or TensorFlow and distributed training on large datasets; ability to influence platform architecture and cross-team roadmaps; and publications in venues such as NeurIPS, ICML, KDD, WWW, ACL, or SIGIR.