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Applied Scientist 2 - Beijing, 中国 - Microsoft
描述
Overview
Software Development Engineer 2 (NLP/Search/Recommendation)
Microsoft Software Technology Center at Asia (STCA) aims to the best user experience for Web Search, Advertisement, Cloud, and Enterprise services. The Search & Distribution Group in STCA has more than 200 scientists and engineers, working on various NLP/Multi-modal techniques and applications.
We're looking for passionate and experienced engineers to help us on our mission of employing deep learning to understand all the data on the web - the largest store of information in human history. With this understanding we power end-user experiences across a variety of NLP/Multi-modal related areas, especially
- Search Relevance
- Natural Language Understanding
- Generative answers with LLM
- Knowledge experiences
- Build up the world-class AI systems with novel NLP techniques and engineering excellence.
- Collaborate with Microsoft Research Asia and partner production teams to explore cutting-edge AI technology and deliver both research and production impact.
Qualifications
Minimum: Master; Preferred: advanced degree and/or industry experience
- Experiences in applying deep learning techniques and drive E2E AI product development.
- 3+ years of working experience in NLP/recommendation/search related areas.
- 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
- Focus on customer impact during design and development
- Ability and motivation to self-teach while entering new domains and managing through ambiguity
#WWE
Responsibilities
Drive core technologies and E2E production delivery by leveraging State-of-Art AI technologies (especially LLMs).
- Address challenges in products through Deep Learning and Reinforcement Learning approaches and transfer novel ideas to production applications.
- Development of deep learning models for Microsoft AI scenarios, including recommendation system, search relevance, knowledge experience, et al.
- Pushing the envelope on deep learning by:
- Defining problems and establishing metrics
- Gathering training data at scale
- Exploring model design and architecture
- Exploring learning objectives and tasks
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