About the job
Microsoft Search & AI platform team delivers the cutting-edge infrastructure for Microsoft search and feeds experience which is used by billions of users. We are looking for a passionate platform developer to join us building the next generation of compute and storage fabric using in-house and open-source technologies. You will be hands-on in a massively scaled and distributed system. You will have the opportunity to work with cutting-edge cloud and machine learning technologies.
Responsibilities
Share accountability of a wide array of assets and be comfortable with learning a broad array of technologies.
Design and implement features in a distributed environment using data to guide and measure success.
Debug and mitigate incidents in live production environments.
Participate in regular team scrums and communicate regularly and clearly with team members and leads.
Look for opportunities to use open-source technologies in our service or to share learnings, services and tools we have created with Microsoft broadly or the open-source community.
Qualifications
Minimum
Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
Preferred
Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
10+ years of experience in building high-performance computing systems, parallel or distributed systems (e.g., in open-source projects or production systems).
Experience on high performance computing (e.g., cache/memory optimization, high-performance GPU programming, compiler-based optimization, fine-grained parallel library and runtime) or distributed systems (e.g., communication optimization, network architecture design, network programming).
Theory and practice on the approximate nearest neighborhood search.
Experience on building Machine Learning/Deep Learning systems.
Experience on performance analysis and optimization for both CPUs and GPUs, as well as good understanding on software-hardware codesign.