Senior Applied Scientist

Overview

Microsoft Advertising empowers businesses to reach global audiences with precision and creativity. We build cutting-edge technology that connects advertisers and consumers across search, display, video, and emerging media platforms. We are seeking a Senior Applied Scientist to drive innovation in training and deploying SLM/LRM for advertising. In this role, you will lead initiatives that address core challenges in ad retrieval, creative generation, relevance, and safety, contributing to the future of responsible, high-impact advertising solutions. 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

- Design, develop, and train Large Language/Retrieval models for advertising scenarios.

- Innovate on model architectures and training strategies to enhance ad retrieval, generation, and relevance at scale.

- Build solutions that proactively address ad safety, including detection of deceptive or harmful content.

 - Collaborate cross-functionally with product and engineering teams to integrate models into production systems and validate their impact.

- Lead technical exploration and research efforts, staying at the forefront of advancements in AI and advertising technology.

- Mentor junior scientists and contribute to a culture of technical excellence and innovation.

Qualifications

Required 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.

- Expertise in training and deploying LLMs.

- Deep understanding of transformer architectures, large-scale distributed training, and model optimization.

- Solid programming skills in Python, C++, and familiarity with frameworks like PyTorch or TensorFlow.

 

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:

- Experience with ads systems, search ranking, recommendation engines, or ad safety models is highly desirable.

- Experience in reinforcement learning, multi-agent systems, or decision-making frameworks. 

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

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Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.