|Job Type:||Full Time|
This role offers you the unique opportunity to work at the interface of research and product to develop novel machine learning solutions for cloud infrastructure (AIOps).
We are a small team in Office 365. We closely collaborate with researchers and core engineering teams across the globe to develop solutions with a high business impact that allow us to scale our service while reducing costs and maintaining reliability.
Office 365 is the largest collaboration service 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. We are a massively distributed cloud service with exabytes data handled by 100s of thousands of servers in 100s of data-centers around the entire globe.
The ideal candidate will have a strong background in machine learning/systems research and the ambition to apply this to production systems. Some of the challenges are: how can we optimize the placement and scheduling of dynamic Office workloads on distributed cloud infrastructure to improve resource utilization?
The algorithms we develop have to capture the complex behaviour of service components, dynamically adapt to changing conditions, and need to be tested/implemented on production systems without compromising the user experience.
Location will be Redmond, US or Cambridge, UK.
- Conduct research to advance the state of the art in machine learning for systems (AIOps).
- Apply that research to develop and deploy scalable models into production which impact billions of people using Office 365.
- Collaborate with team members from other research and engineering teams.
- End-to-end execution of the data science process, from understanding business requirements, data discovery and extraction, to model development and evaluation.
- Data analysis of telemetry signals and derive actionable insights.
- There will be the opportunity to publish, and contribute to scientific conferences
- PhD/MS with relevant work experience in machine learning for systems or related discipline OR equivalent years of industry experience as an engineer.
- Broad and solid understanding of common statistical and machine learning techniques, both classical machine learning and deep learning.
- Strong knowledge in algorithms such as timeseries analysis, anomaly detection, Bayesian optimization, contextual bandits/reinforcement learning, semi-supervised machine learning, constraint optimization, and data visualization.
- Working knowledge on cloud service infrastructure and system design is desirable.
- Experience with working on big data pipelines and cloud solutions.
- Strong analytical, applied research, and communication skills.
- Ability to work independently and in a team, take initiative and lead engagements as required.
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. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. 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.
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
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.