Job Description

Generative AI is transforming how people create, collaborate, and communicate - redefining productivity across Microsoft 365 and our customers globally. At Microsoft, we run the biggest platform for collaboration and productivity in the world with hundreds of millions of consumer/enterprise users. Tackling AI efficiency challenges is crucial for delivering these experiences at scale.
Within our Microsoft wide Systems Innovation initiative, we are working to advance efficiency across AI systems, where we look at novel designs and optimizations across AI stacks: models, AI frameworks, cloud infrastructure, and hardware. We are an Applied Research team driving mid- and long-term product innovations. We closely collaborate with multiple research teams and product groups across the globe who bring a multitude of technical expertise in cloud systems, machine learning and software engineering. We communicate our research both internally and externally through academic publications, open-source releases, blog posts, patents, and industry conferences. Further, we also collaborate with academic and industry partners to advance the state of the art and target material product impact that will affect 100s of millions of customers.
We are looking for a Senior Researcher - Systems Researcher to invent, analyze, and productionize the next generation of serving architectures for transformer-based models across cloud and edge. The candidate will focus on algorithmic and systems innovations, including batching, routing, scheduling, caching, deployment safety, and endpoint configuration, that materially improve latency, throughput, cost, and reliability under real-world SLAs for Microsoft Copilots.
The ideal candidate brings a strong background in distributed systems, operating systems, and/or large-scale ML serving, plus the ambition to translate research into impact in production environments. This role blends rigorous research (theory + measurement) with hands-on engineering, and includes publishing papers, filing patents, and collaborating across research and product teams to advance the state of the art.
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Responsibilities:

• Invent and evaluate algorithms for dynamic batching, routing, and scheduling for transformer inference under multi-tenant SLOs and variable sequence lengths.
• Design and implement caching layers (e.g., KV cache paging/offload, prompt/result caching) and memory pressure controls to maximize GPU/accelerator utilization.
• Develop endpoint configuration policies (e.g., tensor/pipe parallelism, quantization/precision profiles, speculative decoding, chunked/streaming generation) and safe rollout mechanisms.
• Profile and optimize end-to-end serving pipelines: token-level latency, E2E p95/p99, throughput-per-$, cold-start behavior, warm pool strategy, and capacity planning.
• Collaborate with model, kernel, and hardware teams to align serving algorithms with attention/KV innovations and accelerator features.
• Publish research, file patents, and, where appropriate, contribute to open-source serving frameworks.
• Document designs, benchmarks, and operational playbooks; mentor junior researchers/engineers.

Qualifications:
Required Qualifications:

• Doctorate in relevant field

• OR equivalent experience.
• Demonstrated expertise in queuing/scheduling theory and practical request orchestration under SLO constraints.
• Proficiency in C++ and Python for high-performance systems; strong code quality and profiling/debugging skills.
• Proven record of research impact (publications and/or patents) and shipping systems that run at scale.

Other Requirements:
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

• Deep understanding of transformer inference efficiency techniques (attention, paged KV cache, speculative decoding, LoRA, sequence packing/continuous batching, quantization).
• Background in cost/performance modeling, autoscaling, and multi-region DR.
• Hands-on experience with inference serving frameworks (e.g., vLLM, Triton Inference Server, TensorRT-LLM, ONNX Runtime/ORT, Ray Serve, DeepSpeed-MII).
• Familiarity with GPU/accelerator memory management concepts to co-design cache/throughput policies.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the .
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