Software Engineer - Machine Learning Compilers

ARM
Haverhill
1 year ago
Applications closed

Related Jobs

View all jobs

Software Engineer - AI MLOps Oxford, England, United Kingdom

Software Engineer III - MLOps

GenAI Software Engineer/Data Scientist

Lead Software Engineer - Agentic AI/Machine Learning

Lead Software Engineer - MLOps

Senior Software Engineer, Data Science Infra & Optimization

Arm's Machine Learning (ML) Group is seeking a highly motivated and creative Software Engineer to join a team of brilliant engineers located in Cambridge, UK who specialise in ML software.

This role presents an opportunity to chip in to advance ML technology. You will help to build the software that enables development of deep learning applications that form the basis of many ground-breaking technologies like self-driving cars, generative AI engines and ML-powered wearables.

Job Description:

Arm Machine Learning (ML) group is looking for a software engineer who would build a range of innovative software solutions for a variety of markets.

You will apply your experience and insight within this domain to craft and optimise tools for machine learning networks that target Arm’s CPUs, GPUs and NPUs.

If you are interested in this opportunity, make sure to apply soon! We look forward to receiving your application and welcoming you to Arm. You could be joining our highly motivated team and have a marked impact on both strategy and implementation!

Responsibilities:

  • Contribute to deliver production-grade software and push the boundaries of Machine Learning tooling
  • Build, extend and collaborate on innovative ML open source software projects, such as PyTorch
  • Work with other groups in Arm to expand support for Arm architecture and ecosystem

Required skills and experience:

  • Master’s degree in Machine Learning, Electrical Engineering, Engineering Physics, Computer Science, or related field
  • Proven experience with C++ and Python
  • Experience with or interest in compilers
  • Desire to learn new skills and technologies
  • Driven and self-reliant mentality
  • Good interpersonal and communication skills.

"Nice to have" skills and experience:

  • Experience with contributing to open-source projects and working with a broader open-source community
  • Knowledge or curiosity about computer vision, machine learning, their applications and frameworks
  • Experience with Linux and scripting languages, such as shell-scripting

In Return:

Joining our team at Arm Cambridge, UK means becoming part of a collaborative environment filled with diverse and skilled professionals. You will have the chance to work on sector-leading products alongside some of the world's top companies. We value the individual contributions of our team members, and in our small team setup, your work will have a significant impact. Furthermore, this role is a unique opportunity for professional growth in the dynamic and cutting-edge field of ML and AI. Gain a competitive salary, alongside unparalleled learning and networking opportunities from the best in industry.

 

#LI-JB1

 

Accommodations at Arm

At Arm, we want our people toDo Great Things. If you need support or an accommodation toBe Your Brilliant Selfduring 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.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.