Machine Learning Research Engineer

Overview
Do you want to be at the forefront of innovating the latest hardware designs to propel Microsoft's cloud growth? Are you seeking a unique career opportunity that combines both technical capabilities, cross team collaboration, with business insight and strategy? Join our Strategic Planning and Architecture (SPARC) team within Microsoft's Azure Hardware Systems & Infrastructure (AHSI) organization and be a part of the organization behind Microsoft's expanding Cloud Infrastructure and responsible for powering Microsoft's "Intelligent Cloud" mission. Microsoft delivers more than 200 online services to more than one billion individuals worldwide and AHSI is the team behind our expanding cloud infrastructure. We deliver the core infrastructure and foundational technologies for Microsoft's cloud businesses including Microsoft Azure, Bing, MSN, Office 365, OneDrive, Skype, Teams, and Xbox Live. The SPARC organization manages Azure's hardware roadmap from architecture concept through production for all of Microsoft's current and future online services. This role is for a highly motivated Machine Learning Research Engineer with a solid background in neural networks and hardware implementation. You will be involved with both model development, data type analysis, and ML/HW co-design. 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. In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

Responsibilities

1. Driving model/HW codesign.
2. Developing and analysing novel NN architectures.
3. Inventing novel low-precision data formats.
4. Inventing novel model architectures.
5. Collaborating with data scientists and ML researchers.
6. Interfacing with HW architecture teams.
7. Interfacing with SW framework teams.
8. Embody our culture and values.