Senior Machine Learning Research Engineer

Share job

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 Senior 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, 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.

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.

Required Qualifications

• Doctorate or Master's in relevant field

  • OR equivalent experience.

• Experience in ML systems/Model optimizations/Efficient model architecture.

Other Requirements

• 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.

Preferred/Additional Qualifications:

• Master's Degree/PhD in Machine learning, Computer Architecture/Systems, High-Performance Computing or related areas.
• 3+ years 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.
• Solid programming skills in Python/C/C++.
• Experience in implementing low-level linear algebra/BLAS kernels and performance optimizations.
• Outstanding communication skills.

Research Sciences IC4 - The typical base pay range for this role across the U.S. is USD $117,200 - $229,200 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 $153,600 - $250,200 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 will accept applications for the role until December 31, 2024.

#SPARC

• 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.
• Embody our culture and values.