Machine Learning Engineer

Compare the Market
Peterborough
3 weeks ago
Create job alert
Machine Learning Engineer

Join to apply for the Machine Learning Engineer role at Compare the Market.


Get AI-powered advice on this job and more exclusive features.


Job Description

Why this role matters: At Compare the Market, we’re applying AI to real-world problems that help millions of people make smarter financial decisions. As a Machine Learning Engineer, you’ll work at the heart of this transformation—building the infrastructure and tooling that enables our data scientists to move from prototype to production quickly, safely, and at scale.


You’ll be part of a growing ML Engineering team, contributing to a modern MLOps platform and delivering robust ML services in collaboration with product, engineering, and data science colleagues. This is a hands‑on role that’s ideal for someone who wants to grow in a high-impact environment with strong mentorship and real ownership.


What you’ll be doing
ML Engineering & Deployment

  • Develop and maintain machine learning pipelines for training, validation, and deployment
  • Collaborate with data scientists to productionise models and turn prototypes into performant, reliable services
  • Contribute to deployment tooling and automation for both batch and real‑time ML use cases
  • Build monitoring and alerting for model health, performance, and data drift

Platform & Standards

  • Support the evolution of our internal ML platform and development workflows
  • Apply best practices in testing, CI/CD, version control, and infrastructure‑as‑code
  • Contribute to team libraries, reusable components, and shared deployment patterns

Collaboration & Growth

  • Work in cross‑functional teams alongside product managers, engineers, and analysts
  • Participate in design sessions, peer reviews, and sprint planning
  • Learn from and be mentored by experienced ML Engineers and technical leaders

What we’re looking for
Must Have

  • Practical experience deploying ML models into production environments
  • Strong Python development skills and understanding of ML model structures
  • Familiarity with tools such as MLflow, Airflow, SageMaker, or Vertex AI
  • Understanding of CI/CD concepts and basic infrastructure automation
  • Ability to write well‑tested, maintainable, and modular code
  • Strong collaboration skills and a growth mindset
  • A background in software engineering, computer science, or a quantitative field—or equivalent hands‑on experience in ML delivery

Nice to Have

  • Experience working in regulated sectors such as insurance, banking, or financial services
  • Exposure to Databricks, container orchestration (e.g. Kubernetes), or workflow engines (e.g. Argo, Airflow)
  • Familiarity with real‑time model deployment, streaming data, or event‑driven systems (e.g. Kafka, Flink)
  • Interest in MLOps, model governance, and responsible AI practices
  • Understanding of basic model evaluation, drift detection, and monitoring techniques

Why Join Us?

You’ll work on meaningful problems using modern tooling, surrounded by smart, supportive people. We’ll invest in your development, give you the space to grow, and the opportunity to shape how AI is delivered across Compare the Market.


Everyone Is Welcome

We’re committed to building a diverse and inclusive Data & AI team where everyone feels they belong. If this role excites you but you don’t meet every single requirement, we still encourage you to apply. We care about what you can do, not just where you’ve been.


Seniority level

Entry level


Employment type

Full‑time


Job function

Engineering and Information Technology


Industries

Software Development


#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

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.

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.

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.