Address
Form of workOverview
At Microsoft, we operate the largest collaboration services in the world with 100s of millions of consumer/enterprise mailboxes, documents, and conversations. It represents the world's largest platform of human collaboration for personal, business, and educational use. Within our Microsoft wide Systems Innovation initiative, we are working on advancing efficiency across AI systems, where we look at novel designs and optimizations across AI stacks: hardware, cloud infrastructure, AI frameworks, and models. We believe that enabling cross-layer optimization is the key to achieve a step-function improvement in AI efficiency.
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.
We are looking for a Senior Research Scientist to work on efficiency of Large Language Model / Generative AI systems. The ideal candidate will have hands-on experience in LLM frameworks, architecture optimisation, approximate inference, evaluation of LLMs, model distillation/compression, adaptive computation, hardware-aware modelling, and/or AI hardware/accelerator architecture, as well as the motivation and ambition to apply this in a real-world setting.
Responsibilities
Responsibilities
Qualifications
Required Qualifications
Additional or Preferred Qualifications
Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $112,000 - $218,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $145,800 - $238,600 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay
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 Accommodation request form.
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
#M365Core #M365Research
At Microsoft, we operate the largest collaboration services in the world with 100s of millions of consumer/enterprise mailboxes, documents, and conversations. It represents the world's largest platform of human collaboration for personal, business, and educational use. Within our Microsoft wide Systems Innovation initiative, we are working on advancing efficiency across AI systems, where we look at novel designs and optimizations across AI stacks: hardware, cloud infrastructure, AI frameworks, and models. We believe that enabling cross-layer optimization is the key to achieve a step-function improvement in AI efficiency.
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.
We are looking for a Senior Research Scientist to work on efficiency of Large Language Model / Generative AI systems. The ideal candidate will have hands-on experience in LLM frameworks, architecture optimisation, approximate inference, evaluation of LLMs, model distillation/compression, adaptive computation, hardware-aware modelling, and/or AI hardware/accelerator architecture, as well as the motivation and ambition to apply this in a real-world setting.
Responsibilities
Responsibilities
- Conduct novel research to advance the state-of-the-art in inferencing and training optimization across the stack for Large Language Model / Generative AI model systems.
- Work with a small group of fellow Research Scientists and product engineering teams to execute practical solutions for real-world impact.
- Driving 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
- Bachelor's Degree in Statistics, Computer Science, Engineering, Mathematics, Physics, 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.
- 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: statistics, machine learning, and optimization.
Additional or Preferred Qualifications
- PhD in Statistics, Computer Science, Engineering, Mathematics, Physics, or related field AND 3+ years related experience (e.g., statistics predictive analytics, research) OR equivalent experience.
- Knowledge of state-of-the-art Large Language Models (LLMs)
- Experience in improving the design and training/inference efficiency of generative AI systems, including LLM agent frameworks, adaptive computation, model architecture optimization, etc.
- Experience with one or more programming languages: Python, C/C++, Java, etc.
- Ability to work independently and in a team, take initiative and lead engagements as required.
Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $112,000 - $218,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $145,800 - $238,600 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay
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 Accommodation request form.
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
#M365Core #M365Research
