Senior ML Engineer

ARM
Ely
1 year ago
Applications closed

Related Jobs

View all jobs

Senior ML Engineer for Robotics & MLOps (Hybrid)

Senior ML Engineer - Production ML & MLOps on Databricks

Senior ML Engineer: Audio & MLOps in Insurance

Senior ML Engineer — MLOps Platform & Templates

Senior ML Engineer – End-to-End AI & MLOps (Audio)

Senior Machine Learning Engineer

Job Description:

Are you interested in empowering Arm’s engineering teams to create products using the power of machine learning and statistics? Are you passionate about working in a team that thrives on creativity, innovation, and diversity?

Arm is using Machine Learning and Data Science techniques to empower our engineers to make data-driven decisions and is building automated workflows that enable our engineers to deliver more complex products. Based in the “Machine Learning for Verification” (ML4V) team in Arm’s Productivity Engineering group, this role will enable you to directly impact Arm’s engineering processes and will help improve future generations of Arm-based technology used by 70% of the world’s population.

As a Senior ML Engineer, you will be working across Arm engineering workflows ensuring that our solutions are optimized and delivering maximum value. You will be part of a highly interdisciplinary team that deliver compute, data science, research, and engineering. You will be responsible for productizing data pipelines and models and deploying them into a production environment so that they deliver the maximum value to our teams. Because we have such a diverse team, with many areas of speciality, there will be opportunities to mentor and coach, as well as to develop your own knowledge and understanding.

Our technical stack leverages open-source python libraries, using distributed compute across on-prem and cloud environments.

Responsibilities:

  • Building and optimising ML processes across the whole ML lifecycle, from proof-of-concept to production
  • Writing and deploying scalable and resilient solutions
  • Collaborating with our diverse teams including Verification engineers and data scientists
  • Communicating your work to technical and non-technical collaborators.

Required Skills and Experience:

  • Demonstrable experience using python to deploy and run machine learning processes in a production environment.
  • Demonstrable knowledge of statistics and machine learning (eg, Dimensionality Reduction, Hypothesis Testing, Imputation, Clustering methods, Tree Based Algorithms, Neural Networks)
  • Experience with Data Science and Machine Learning Frameworks (e.g Pandas, Scikit-Learn)
  • Experience with Cloud platforms like AWS, GCP, Azure.
  • Experience with software development & CI/CD processes: (Git, testing, releasing code)
  • Comfortable working within a Linux environment

“Nice To Have” Skills and Experience:

  • A master or PhD degree in a relevant field, such as computer science, physics, statistics, maths etc
  • Experience working with imbalanced datasets.
  • Experience with distributed compute using pyspark
  • Experience with Databases (no-sql, sql)
  • Experience serving models through a service orientated architecture
  • Experience with Serving ML models in a streaming environment.
  • Experience mentoring and/or coaching colleagues.

 

In Return: 

Arm is a global, diverse organization of dedicated, innovative, and highly capable people. We believe great ideas come from a vibrant and inclusive workplace where everyone can grow, succeed, and share their outstanding contributions! You will join a multi-cultural team with varied skills and experience. You will have access to a huge variety of technologies and systems to learn and will be supported every step of the way!

 

 

 

 

 

 

 

 

 

#LI-KD1

 

 

 

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.

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

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.