Principal Applied Scientist
Microsoft
Principal Applied Scientist
Beijing, China
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Overview
We are seeking an Applied Scientist / AI Architect with strong hands-on experience in building and optimizing large language models (LLMs), agentic AI systems, and end-to-end model training workflows. This role is ideal for scientists with a solid applied background who can translate state-of-the-art research into real-world impact. A research-oriented mindset with publications in top AI/ML venues is highly preferred but not strictly required.
Our mission is to innovate in collaborative office area by exploring LLM and agentic AI. You will collaborate closely with product, engineering, and research teams to ship intelligent, reliable, and innovative AI capabilities at scale.
Qualifications
• M.S. or Ph.D. in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
• 5+ years of experience in applied machine learning, with a focus on LLMs, agent systems, or reinforcement learning.
• Strong hands-on experience with prompt engineering, context engineering, retrieval-augmented generation (RAG), tool use, planning agents, and long-context modeling, etc.
• Familiarity with model training pipelines using PyTorch, TensorFlow, JAX, or similar frameworks, evaluation strategies, and model deployment best practices.
• Solid publication record (e.g., NeurIPS, ICLR, ACL, ICML, EMNLP) is a plus, but emphasis is placed on practical contributions.
• Strong coding and debugging skills, and comfort working in cross-functional, agile environments.
Responsibilities
• Design and implement advanced LLM-based architectures and agentic systems for real-world product scenarios.
• Translate research breakthroughs into production-ready algorithms, contributing to core capabilities such as reasoning, planning, long-term memory, code-gen based design.
• Monitor and improve model performance post-deployment through data-driven iteration and error analysis.
• Collaborate across teams to deliver robust, scalable models aligned with product objectives and user value.
• Contribute to the organization’s scientific direction by identifying research opportunities that drive long-term differentiation.