Senior Machine Learning Engineer

Coventry
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Machine Learning Engineer - Research

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Stark Group are hiring now for a Senior Machine Learning Engineer working from home (remote - UK based).

Stark a leading building materials distributor in Northern Europe, is looking for a Senior Machine Learning Engineer who's as passionate about great service as we are.

We provide a fulfilling and enjoyable work environment with ample opportunities for career growth, along with a competitive salary, staff discount, life assurance, and 34 days of holiday (inclusive of bank holidays).

What you'll be doing:

You will play a lead role designing, developing and deploying AI solutions on a product basis and work to further the progress of the AI and Advanced Analytics team. This includes setting direction and being responsible for Machine Learning projects, collaborating with other data areas, our partners and the wider business to transform AI prototypes into production and having responsibility for the management and improvement of deployed models and data pipelines.

You will be required to:

Play a lead role in the design and development of overall AI systems using data science and software engineering best practice and techniques to increase profit, decrease cost and optimize workflow of Stark Group.

Work on a range of projects across the business from Supply Chain to Marketing to positively impact financial performance.

Collaborate and direct Data Scientists to turn prototypes into production-grade AI systems, increasing efficiency in the production of outputs and ensuring that the business has the most up-to-date data available upon which to base key decisions.

Writing production grade Python and SQL for feature engineering and model pipelines, incorporating best practices such as packaging, unit testing, version control and logging ensure that the software we deliver is of high quality, extensible and reproducible.

Collaborate with digital areas to ensure interpretability and reliability of AI systems reducing the need for costly manual intervention.

Play a leading role in contributing to and improving overall team standards and best practice to improve the quality and efficiency of how the team operates and our outputs. Innovate to drive best practices for data within Stark through use of new technologies, Machine Learning and AI, bringing other areas along with us.

Lead on collaborative knowledge sharing and improvements to working practices order to further personal development and data science within Stark.

Nurture and develop good working relationships with the team, project stakeholders and customers to ensure smooth product delivery.

What you'll need to have:

MSc in a relevant discipline (e.g. Computer Science, Data Science, Information technology etc.) or relevant experience.

Experience in a hands-on developer role within an AI team.

Previous programming experience with data in Python and SQL, ideally in Software Engineering, Data Engineering or Data Science.

Knowledge and experience of the Python data & AI stack

Knowledge and experience of development and version control tools and workflows (e.g. Git, Feature branch)

Experience of MLOps and associated tools such as Azure DevOps/Github, MLFlow, Azure ML

Experience working with large datasets/big data architectures; particularly Pyspark / Databricks.

Experience deploying container technologies (e.g. Docker, Kubernetes)

Experience playing a lead role on technical AI projects.

Excellent communication skills with both technical and non-technical audiences

High level of accuracy of work and attention to detail.

Ability and desire to self-learn and pick up emerging technologies.

Positive attitude and outlook and enjoy working as part of a team to share knowledge and ideas.

Desirable

Experience of Data & AI with Azure, particularly Azure ML

Experience working with and deploying LLMs

Experience with deploying cloud resources using infrastructure-as-code, particularly Azure/Bicep

What's in it for you:

Discretionary bonus

A wide range of voluntary benefits including holiday buying, discounted gym membership, car salary sacrifice scheme, Cycle2Work, Benenden Healthcare and more.

Access to a wealth of health and wellbeing services including access to online GP appointments and mental health support

Generous employee discounts

Access to discounts with hundreds of your favourite high street and online retailers

Retirement savings plan

Life assurance

Enhanced maternity/paternity/adoption leave for anyone expecting or adopting a child

Why STARK?

We're proud to be part of STARK Building Materials UK and dedicated to providing top-quality products and exceptional service to our customers. We're a friendly and collaborative team, passionate about what we do and committed to doing it well.

If you're ready to take your career to the next level and join a team that is dedicated to providing great service, we want to hear from you. Apply today

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.

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

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.