Senior Applied Scientist
Senior Applied Scientist
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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.
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.
Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
Responsibilities
- Model Optimization & Deployment:
- Design and implement efficient workflows for training, distillation, and fine-tuning Small and Large Language Models (SLMs), leveraging techniques such as LoRA, QLoRA, and instruction tuning.
- Apply model compression strategies—including quantization (e.g., GPTQ, AWQ) and pruning—to reduce inference costs and improve latency.
- Optimize LLM inference performance using frameworks like vLLM and TensorRT-LLM (TRT-LLM) to enable scalable, low-latency deployment.
- Build robust and scalable inference systems tailored to heterogeneous production environments, with a strong focus on performance, cost-efficiency, and stability.
- Evaluation & Data Management：
- Develop evaluation datasets and metrics to assess model performance in real-world product scenarios.
- Build and maintain end-to-end machine learning pipelines encompassing data preprocessing, training, validation, and deployment.
- Cross-functional Collaboration：
- Collaborate closely with product managers, engineers, and research scientists to translate business needs into impactful AI solutions, driving real-world adoption and seamless product integration.
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.
- Solid programming skills with hands-on experience in managing large-scale data and machine learning pipelines.
- Deep understanding of open-source ML frameworks such as PyTorch, vLLM, and TensorRT-LLM (TRT-LLM).
- Solid knowledge of model optimization techniques, including quantization, pruning, and efficient inference.
Additional or preferred qualifications:
- Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
- 1+ years of experience optimizing LLM inference using frameworks like vLLM or TRT-LLM.
- Practical experience in model compression and deployment within production systems.
- Experience designing agentic AI systems, such as multi-agent orchestration, tool usage, planning, and reasoning.
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
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.
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