Staff Machine Learning Architect - Assembly Coding and Performance Engineer

Cambridge
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Machine Learning Engineer

Freelance Spatial AI and Machine Learning Consulta - Remote

Lead Data Scientist

Data Scientist (production grade ML)

Principal Data Scientist

Job Overview:

High-performance ML workloads on Arm CPUs requires the co-development of algorithms and highly optimized CPU kernels. In CT-ML (Central Technology, Machine Learning), rapid kernel prototyping is crucial for exploring algorithms and assessing trade-offs between model accuracy and performance. Successful prototypes are essential to drive future CPU architecture development and also deliverables to Central Engineering for final production.

Responsibilities:

This position is part of a dedicated team within the CT-ML group to focus on analyzing ML workload, rapid prototyping of highly optimized CPU kernels to drive model performance and accuracies.

Required Skills and Experience :

Strong interest and passion for implementing high-performance kernel code in a dynamic environment.

4+ years experience in implementing high performance CPU kernel with vector and matrix extensions.

Experience measuring and understanding performance

Experience in creating an efficient kernel code development framework including tools and testing

Deep understanding on CPU architecture

“Nice To Have” Skills and Experience :

Knowledge of ML models and algorithm is a plus

Advanced degree or equivalent experience in Computer Architecture and Software are a plus

In Return:

Arm is committed to global talent acquisition, offering an attractive relocation package. With offices around the world, Arm is a diverse organization of dedicated, creative and highly talented engineers. By enabling a dynamic, inclusive, meritocratic, and open workplace, where all our people can grow and succeed, we encourage our people to share their unrivaled contributions to Arm's success in the global marketplace.

 

#LI-TE!

 

Accommodations at Arm

At Arm, we want our people to Do Great Things. If you need support or an accommodation to Be Your Brilliant Self during the recruitment process, please email . To note, by sending us the requested information, you consent to its use by Arm to arrange for appropriate accommodations. All accommodation requests will be treated with confidentiality, and information concerning these requests will only be disclosed as necessary to provide the accommodation. Although this is not an exhaustive list, examples of support include breaks between interviews, having documents read aloud or office accessibility. Please email us about anything we can do to accommodate you during the recruitment process.

Hybrid Working at Arm

Arm’s approach to hybrid working is designed to create a working environment that supports both high performance and personal wellbeing. We believe in bringing people together face to face to enable us to work at pace, whilst recognizing the value of flexibility. Within that framework, we empower groups/teams to determine their own hybrid working patterns, depending on the work and the team’s needs. Details of what this means for each role will be shared upon application. In some cases, the flexibility we can offer is limited by local legal, regulatory, tax, or other considerations, and where this is the case, we will collaborate with you to find the best solution. Please talk to us to find out more about what this could look like for you.

Equal Opportunities at Arm

Arm is an equal opportunity employer, committed to providing an environment of mutual respect where equal opportunities are available to all applicants and colleagues. We are a diverse organization of dedicated and innovative individuals, and don’t discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.