Lead Machine Learning Engineer

Glasgow
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Are you a seasoned Machine Learning Engineer ready to take the next step in your career by productionising GenAI and Recommender Systems at huge scale?

Do you have a passion for machine learning and a keen interest in the transformative potential of generative AI?

About the Role:

You'll join a global online marketplace as a Lead Machine Learning Engineer in an ML Enablement team. In this role, you'll be at the forefront of productionising GenAI and Recommender Systems at scale.

Your expertise will drive significant change and help shape the future of the their business and how hundreds of millions of customers interact with their platform.

Key Responsibilities

Productionise GenAI and Recommender Systems: Develop and implement scalable solutions for a global platform.

MLOps Focus: Utilise MLflow, SageMaker, and machine learning libraries to streamline and optimise ML operations.

Collaborate and Innovate: Work with a team of brilliant minds on projects that directly impact hundreds of millions of users worldwide.

Technical Requirements

Machine Learning Expertise: Previous experience as a Senior Data Scientist or ML Engineer, with hands-on experience deploying ML models in production within a commercial environment. Strong understanding of ML models and their applications.

Programming and Frameworks: Proficiency in Python and SQL,. Hands-on experience with ML frameworks like TensorFlow, PyTorch, and Scikit-Learn.

Cloud and Containerisation: Experience with cloud platforms (AWS, GCP, or Azure) and containerisation technologies (Docker, Kubernetes).

MLOps and Responsible AI: Familiarity with CI/CD pipelines, model registries, ML observability tools, responsible AI principles, model monitoring, and data privacy best practices.

Compensation: Base salary of £90-95k, plus bonuses and a host of other benefits including ability to travel internationally to global offices, or work from anywhere globally for 1 month per year.

Location: London, Hybrid. 2 days on-site per week.

Apply now for immediate consideration

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 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.

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.