Sign up with

Already have an account? Log in here

Need some help?
Talk to us at +91 7670800001

Applied Scientist 2 at Microsoft

Beijing, Beijing, China -

Full Time

Start Date

Immediate

Expiry Date

27 Dec, 25

Salary

0.0

Posted On

28 Sep, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Applied Science, Machine Learning, Language Models, Training Optimization, Inference Optimization, Supervised Fine-Tuning, Instruction Tuning, Reinforcement Learning, Model Efficiency, Compression Strategies, GPTQ, AWQ, Pruning, AI Systems, Engineering, Enterprise AI

Industry

Software Development

Description

The Copilot Asia ML Team is driving the next generation of intelligent assistant infrastructure, powering Microsoft Copilot experiences across the enterprise. Our mission is to build foundational language models that make Copilot more helpful, responsive, and accessible to millions of users worldwide. We are looking for Applied Scientists to pioneer innovations in scalable training and inference optimization for both Small and Large Language Models (SLMs/LLMs). In this role, you will directly shape the core platform capabilities of Copilot, influencing how organizations interact with AI-driven assistants every day. Our work spans the entire model lifecycle—from supervised fine-tuning to advanced post-training techniques such as instruction tuning, reinforcement learning, and alignment. We also push the boundaries of model efficiency with cutting-edge compression strategies, including GPTQ, AWQ, and pruning, to deliver faster, more cost-effective inference at scale. If you’re passionate about creating intelligent assistant systems that combine deep model expertise with world-class engineering, and want to shape the future of enterprise AI, we’d love to have you on our team.

Responsibilities

The role involves pioneering innovations in scalable training and inference optimization for Small and Large Language Models. You will shape the core platform capabilities of Copilot, influencing AI-driven assistant interactions.