Senior Machine Learning Research Scientist

1 Month ago • 3 Years + • DevOps

Job Summary

Job Description

Microsoft's Azure Hardware Systems & Infrastructure (AHSI) seeks a Senior Machine Learning Research Scientist to drive model/hardware co-design, analyze novel LLM architectures, and invent efficient model architectures (e.g., sparse LLMs). Responsibilities include developing low-precision data formats, collaborating with data scientists and hardware/software teams, and optimizing CNN/transformer architectures. The ideal candidate possesses a Master's/PhD in a related field, 3+ years of ML systems experience, and expertise in frameworks like PyTorch/TensorFlow/TensorRT.
Must have:
  • Master's/PhD in ML, Computer Architecture, or related field
  • 3+ years ML systems/model optimization experience
  • Experience with PyTorch/TensorFlow/TensorRT
  • Deep knowledge of CNN/transformer architecture and optimization
  • Strong programming skills in Python/C/C++
Good to have:
  • Experience implementing low-level linear algebra kernels
  • Knowledge of GPU/TPU accelerator architecture
Perks:
  • Industry leading healthcare
  • Educational resources
  • Discounts on products and services
  • Savings and investments
  • Maternity and paternity leave
  • Generous time away
  • Giving programs
  • Networking opportunities

Job Details

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 on-line servicesThis 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. 

Qualifications

Master's Degree/PhD in Machine learning, Computer Architecture/Systems, High-Performance Computing or related areas. 

3+ years of 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 

Knowledge of GPU, TPU or similar NPU accelerator architecture   

Outstanding communication skills 

 

#SPARCjobs

 

Responsibilities

  • Driving model/hardware codesign 
  • Developing and analysing novel LLM architectures 
  • Inventing novel low-precision data/number formats for training/inference SOTA LLMs  
  • Inventing novel efficient model architectures (e.g., sparse LLMs, attention architecture) 
  • Collaborating with data scientists and ML researchers 
  • Interfacing with HW architecture teams 
  • Interfacing with SW framework teams 
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
Industry leading healthcare
Educational resources
Discounts on products and services
Savings and investments
Maternity and paternity leave
Generous time away
Giving programs
Opportunities to network and connect

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