Senior Machine Learning Research Engineer

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 on-line services.  This role is for a highly motivated Machine Learning Engineer with a strong background in neural networks and hardware implementation. You will be involved with both model development, data type analysis, ML/HW co-design. 

• MS/PhD in Machine learning, Computer Architecture/Systems, High-Performance Computing or related areas 
• Extensive  experience in ML systems/Model optimizations/Efficient model architecture 
• Track record of original research and delivering novel results in ML systems area 
• Hands on experience with frameworks such as PyTorch/TensorFlow/TensorRT 
• Deep knowledge of CNN/transformer architecture and optimization strategies – quantization, sparsity, NAS, sharding, KV Cache, Flash Attention 
• Strong programming skills in Python/C/C++ 
• Experience in implementing low-level linear algebra/BLAS kernels and performance optimisations 
• Outstanding communication skills 

• Driving model/HW codesign 
• Developing and analysing novel NN architectures 
• Inventing novel low-precision data formats 
• Inventing novel model architectures 
• Collaborating with data scientists and ML researchers 
• Interfacing with HW architecture teams 
• Interfacing with SW framework teams