ARM Senior / Staff Machine Learning Engineer 

United Kingdom, England, Cambridge 

32829565

Responsibilities :

Your responsibilities involve working with major ML frameworks (PyTorch, TensorFlow, etc.) to port and develop ML networks, optimize and quantize models for efficient execution on Arm platforms, and help ensure multiple Arm products are designed to perform effectively for machine learning. As an in-depth technical responsibility, you will need to deeply understand the complex applications you analyze and communicate them in their simplest form to contribute to product designs, allowing you to influence both IP and system architecture.

Required Skills and Experience :

• A background in computer science, software engineering or other comparable skills
• Experience training and debugging neural networks with TensorFlow and PyTorch using Python
• Understanding, deploying, and optimizing Large Language Models (LLMs) and Generative AI algorithms.
• Experience using software development platforms and continuous integration systems
• Familiarity with Linux and cloud services
• Have a strong attention to detail to ensure use cases you investigate are well understood and the critical areas needing improvement are understood

Nice To Have Skills and Experience :

• Experience of the inner workings of Pytorch, Tensorflow, Executorch and Tensorflow Lite
• Experience of developing and maintaining CI/testing components to improve automation of model analysis
• Good knowledge of Python for working with ML frameworks
• Good knowledge of C++ for working with optimised ML libraries
• Previous experience of machine learning projects
• Experience with deployment optimizations on machine learning models

In Return :

Working closely with experts in ML and software and hardware optimisation - a truly multi-discipline environment - you will have the chance to explore existing or build new machine learning techniques, while helping unpick the complex world of use-cases spanning mobile phones, servers, autonomous driving vehicles, and low-power embedded devices