Join Microsoft Ads as a Senior Applied Scientist and shape the future of a massive digital advertising ecosystem. This role offers a unique opportunity to blend cutting-edge research with tangible product impact, focusing on ad retrieval, matching, ranking, and generation systems. You will pioneer novel machine learning and AI solutions, driving measurable improvements for both users and the business. Leverage and advance state-of-the-art technologies like LLMs, SLMs, and LRMs to tackle complex, web-scale challenges. Your contributions will directly enhance ad discovery, matching, ranking, and generation, ultimately improving user experience, advertiser ROI, and the overall efficiency of the Microsoft Ads platform.

We seek scientists who excel in both deep scientific exploration and practical application. The ideal candidate is driven by scientific curiosity, but equally motivated by delivering solutions, quantifying impact, and iterating based on real-world feedback. Thriving in a collaborative, cross-functional environment is key, as you'll transform innovative ideas into production systems that handle billions of requests daily.

Microsoft is committed to empowering every person and organization globally. Our team embodies a growth mindset, fostering innovation to empower others and collaborating to achieve shared objectives. Our core values of respect, integrity, and accountability cultivate an inclusive culture where everyone can flourish.

Advance research and development across ad retrieval, ranking, matching, and generative models.

Leverage and enhance SLMs, LLMs, and LRMs by training, fine-tuning, and productionizing models.

Evolve the Ads ranking platform to improve usability, reliability, scalability, efficiency, and architectural coherence.

Provide technical leadership, setting project direction, mentoring distributed teams, and influencing cross-organizational strategy.

Stay abreast of AI research trends to ensure the group's solutions remain state-of-the-art.

Collaborate effectively with research and engineering teams.

Required Qualifications:

- A Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field, coupled with 4+ years of relevant experience (e.g., statistics, predictive analytics, research). - Alternatively, a Master's Degree in a related field with 3+ years of relevant experience. - Or, a Doctorate in a related field with 1+ year of relevant experience. - Equivalent experience will also be considered.

Other Requirements: - Must meet Microsoft, customer, and/or government security screening requirements, including the Microsoft Cloud Background Check.

Preferred Qualifications:

- A Master's Degree in a relevant field with 5+ years of experience, or a Doctorate with 3+ years of experience. - 6+ years of experience in Machine Learning with a strong track record of deploying large-scale models to production. - Expertise in optimizing model training and inference. - Demonstrated ability to influence platform architecture and align cross-team roadmaps. - A history of publications in top-tier venues (e.g., NeurIPS, ICML, KDD, WWW, ACL, SIGIR). - Hands-on experience with SLM/LLM/LRM training, fine-tuning, and post-training. - Experience designing and scaling recommendation systems for massive datasets and multi-stage ranking pipelines. - Proficiency with deep learning frameworks like PyTorch, Hugging Face, and TensorFlow, including distributed training on large datasets.