Senior Researcher

4 weeks ago

Cambridge, United Kingdom Microsoft Full time

Generative AI is revolutionizing how we create, share, work with, and consume content. 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 believe that enabling cross-layer optimization is the key to achieve a step-function improvement in AI efficiency and to provide generative AI experiences to more users globally.

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.

Location will be Cambridge, UK.

M365Core #M365Research #Research

**Responsibilities**:

- Conduct novel research to advance the state-of-the-art in end-to-end efficiency for Large Language Model / Generative AI experiences to enable their deployment at scale.
- Work with a small group of fellow research scientists and product engineering teams to execute practical solutions for real-world impact.
- Drive the end-to-end research agenda from establishing the problem definition to building algorithms and models.
- Publish and contribute to top scientific conferences and journals.

**Qualifications**:
**Required** **Qualifications**
- PhD in Computer Science, Statistics, Engineering, Mathematics, Physics, or related field
- Research experience and publications in top conferences/journals (NeurIPS, ICML, ICLR, AISTATS, ACL, EMNLP, NAACL, ISCA, MICRO, ASPLOS, HPCA, SOSP, OSDI, NSDI, etc.) in at least one of the following areas: natural language processing, statistics, machine learning, and optimization.

**Preferred Qualifications**
- PhD in Statistics, Computer Science, Engineering, Mathematics, Physics, or related field AND related experience (e.g., statistics predictive analytics, research) OR equivalent experience.
- Hands on experience in improving the design and efficiency of generative AI systems and related frameworks and toolkits such as LLM agents and RAG based systems.
- Familiarity with LLMs such as the OpenAI GPT models, LLaMa etc., model fine-tuning techniques (LoRa, QLoRa), prompting techniques (Chain of Thought, ReACT etc.).
- Ability to work independently and in a team, take initiative and lead engagements as required.

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