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Cambridge Internship in ML Model Optimization

England, United Kingdom (On-site)

Cambridge Internship in ML Model Optimization

1 Hour ago • Upto 1 Years • DevOps • Undisclosed

About the job

5 skills required for this role

Add these skills to join the top 1% applicants for this job

azure

python

pytorch

communication

team-management

Job Description

This internship within Microsoft's Azure Hardware Systems & Infrastructure (AHSI) organization, specifically the Strategic Planning and Architecture (SPARC) team, focuses on model compression and optimization for Large Language Models (LLMs). The intern will research and develop quantization flows for LLM inference and training, design, implement, and evaluate the performance of quantized SOTA LLMs, and present findings in technical documents and presentations. The role involves hands-on experience with quantization of LLMs, model compression, low-precision data types, and potentially PyTorch and Python. The internship is located in Cambridge, UK and is open to Masters/PhD students in Computer Science/Machine Learning or related fields.

Must have:

Masters/PhD in CS/ML
Experience in LLM quantization and model compression
Knowledge of low-precision data types
Research and development of quantization flow
Performance evaluation of quantized LLMs
Technical document and presentation writing

Good to have:

PyTorch
Python
SW Tool development experience
Excellent communication skills

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

Overview

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. We are seeking a masters/PhD student to join us in Cambridge winter/spring 2025 to work on model compression and optimization for LLMs, covering topics such as post training quantization and quantization aware training. You will be joining a welcoming and highly interdisciplinary team and work on creative and challenging problems during your internship.