Senior Researcher - Efficiency for Large Language Models

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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 - Efficiency for Large Language Models to explore model/system-level optimizations to deliver significant efficiency gains for Large Language Models and Generative AI experiences. The ideal candidate will have strong knowledge of state-of-the-art and emerging Large Language Models, LLM architectures & optimizations, as well as hands-on experience in LLM frameworks and evaluation. We are seeking someone with an interest to work at the intersection of research and product with the ambition to apply this research into a real-world setting.

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. Have a look at this link for reading: https://www.microsoft.com/en-us/research/group/systems-innovation/

Required Qualifications:

• Doctorate in Computer Science, Machine Learning, Statistics, Engineering, Mathematics, Physics, or related field
  • OR equivalent experience.
• 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.
• Solid knowledge of state-of-the-art and emerging Large Language Models (LLMs), including their application in complex systems.
  
• Solid coding and engineering skills to design experiments and help to drive research into product.

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:

• Doctorate in Statistics, Computer Science, Engineering, Mathematics, Physics, or related field AND 2+ years 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
• 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.

#M365Core #M365Research #Research

• Conduct novel research to advance the state-of-the-art in 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.