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Microsoft Corporation Senior Applied Scientist(Ads) in Beijing, China

Are you interested in being part of a team with close to $20B in revenue and rapid growth? Are you looking for joining a fun and fast paced environment, where people are empowered to innovate? Are you excited about cutting-edge LLM/LMM innovation and applications?

Microsoft Advertising is now the 4th largest Ad network in the world with Search and Open-Web programmatic (MSAN, Xandr) as core pillars. Our team provides efficient recommendations to help our customers to achieve their goals. The team is a world-class R&D team of passionate and talented scientists and engineers who aspire to solve challenging problems using advanced ML technologies on top of big data.

We are looking for Senior Applied Scientist with background in Machine Learning, Reinforcement Learning, Causal Inference, Large Language models, Natural Language Processing (NLP), Natural Language Generation (NLG) techniques, Control Theory, Data Science, Data Mining, or related fields. Candidates should be passionate about artificial intelligence and optimization at web scale. They will play a key role in driving algorithmic improvements to online and offline systems, develop and deliver robust and scalable solutions, make direct impact to both user and advertisers experience, and continually increase the revenue for Microsoft Advertising.

Responsibilities

Our team provides efficient recommendations to help advertisers improve campaign performance and drive Microsoft Advertising revenue growth. We are looking for applied scientist in deep learning and data analytics related areas. A successful candidate will play a key role in driving algorithmic and modeling improvement to the system, analyze performance and identify opportunities based on offline and online testing, develop and deliver robust and scalable solutions, make direct impact to both user and advertisers experience, and continually increase the revenue for Microsoft Advertising. The candidate should also have excellent communication, collaboration and analytical skills. More specifically:

• Design and develop deep learning models for user-to-ad matching/ranking, user response prediction, ad creative generation, keyword expansion, keyword categorization, audience targeting, traffic estimation and etc.

• Analyze ads campaign performance and identify growth opportunities using advanced statistics

• Design and develop robust and scalable model serving solutions and pipelines

• Design and develop large-scale online A/B test infrastructure for demand experiment

• Work closely with various teams to understand common needs and build technical roadmap for addressing them.

Qualifications

• M.S. or PhD degree in CS/EE or related field AND 6+ years related experience.

• Experiences in applying deep learning techniques and drive E2E AI product development (LLM, Search, NLP, etc).

• Excellent coding experience in Python, C++, C#, C or Java

• Familiar with common machine learning, deep learning frameworks like Pytorch/Tensorflow

• Passionate and self-motivated, Ability to effectively collaborate and ship production features in a multi-project, fast-paced team environment.

• Good communication skills, both verbal and written

#WWE

Microsoft is an equal opportunity employer. Consistent with applicable law, 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 and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations (https://careers.microsoft.com/v2/global/en/accessibility.html) .

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