Machine Learning Engineer - Personalisation

Up Closets of North Cincinnati
London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer - C++

London

About Cleo

At Cleo, were not just building another fintech app. Were embarking on a mission to fundamentally change humanitys relationship with money. Imagine a world where everyone, regardless of background or income, has access to a hyper-intelligent financial advisor in their pocket. Thats the future were creating.

Cleo is a rare success story: a profitable, fast-growing unicorn with over $200 million in ARR and growing over 2x year-over-year. This isnt just a job; its a chance to join a team of brilliant, driven individuals who are passionate about making a real difference. We have an exceptionally high bar for talent, seeking individuals who are not only at the top of their field but also embody our culture of collaboration and positive impact.

If you’re driven by complex challenges that push your expertise, the chance to shape something truly transformative, and the potential to share in Cleo’s success as we scale, while growing alongside a company that’s scaling fast, this might be your perfect fit.

Machine Learning Engineers at Cleo work on building novel solutions to real-world problems. This really does vary but could be: creating chatbots to coach our users around their financial health, creating classifiers to better understand transaction data or even optimising transactions within our payments platform.

Ultimately, we’re looking for a brilliant Machine Learning Engineer to join us on our mission to fight for the worlds financial health. You’ll be leading technical work within a team of adaptable, creative and product-focused engineers, who train & integrate cutting edge machine learning across a variety of products and deploy them into production for millions of users. We understand our customers, we understand their pain, and we are passionate about helping them.

What you’ll be doing

  • Training and fine-tuning models to help customers get more value from our chatbot and app through deeper personalisation, creating a smarter & more engaging experience, recommending the right content and features to make users love Cleo
  • Integrating LLMs hosted by OpenAI, Anthropic, GCP, AWS
  • Working cross-functionally with backend engineers, data analysts, UX writers, product managers, and others to ship features that improve our users’ financial health
  • Driving the adoption of appropriate state-of-the-art techniques for recommendation, message campaign optimisation, and contextual bandits.
  • Communicating the team’s successes and learnings at the company level & beyond.
  • Developing a holistic view of personalisation and user-level features across Cleo, taking the initiative to extend existing approaches to benefit new areas of the app and conversations.
  • Supporting ML Engineers around problem framing, ML modelling, and evaluation

Here are some examples, big and small, of the kinds of product feature work our ML Engineers have taken part in over the last year:

  • Designed and implemented AI agents to analyse and extract insights from users’ transactional data.
  • Developed models to interpret transactional data, enhancing the understanding of users’ finances. Think about your bank statement—how often do you not recognise a transaction on first review?
  • Created contextual intent classifiers to understand user conversations with Cleo, enabling tailored and accurate platform responses.
  • Engineered ML models to identify and deliver relevant actions to users within Cleo, ensuring a seamless, context-aware conversational experience.
  • Built models to evaluate risk in customer interactions with bank transaction features and user activities.
  • Developed optimisation models to improve payment success rates for customers while minimising business costs, tackling this as a two-sided optimisation challenge.

Whatever problem you tackle, and whichever team you join, your work will directly impact those most in need, helping to improve their financial health.

What you’ll need

  • 3-5 years of experience in data science, machine learning engineering, or related roles
  • Excellent knowledge of both Data Science (python, SQL) and production tools
  • Strong ability to communicate findings to non-technical stakeholders
  • Experience deploying machine learning models into production; familiarity with Docker containers and container orchestration tools is a plus

Nice to have

  • Experience with recommender systems, personalisation, or ad optimisation

What do you get for all your hard work?

  • A competitive compensation package (base + equity) with bi-annual reviews, aligned to our quarterly OKR planning cycles. You can view our public progression framework and salary bandings here: https://cleo-ai.progressionapp.com/
  • Work at one of the fastest-growing tech startups, backed by top VC firms, Balderton & EQT Ventures
  • A clear progression plan.We want you to keep growing. That means trying new things, leading others, challenging the status quo and owning your impact. Always with our complete support.
  • Flexibility. We can’t fight for the world’s financial health if we’re not healthy ourselves. We work with everyone to make sure they have the balance they need to do their best work
  • Work where you work best.We’re a globally distributed team. If you live in London we have a hybrid approach, we’d love you to spend one day a week or more in our beautiful office. If you’re outside of London, we’ll encourage you to spend a couple of days with us a few times per year. And we’ll cover your travel costs, naturally.
  • Other benefits;
  • Company-wide performance reviews every 6 months
  • Generous pay increases for high-performing team members
  • Equity top-ups for team members getting promoted
  • 25 days annual leave a year + public holidays (+ an additional day for every year you spend at Cleo, up to 30 days)
  • 6% employer-matched pension in the UK
  • Private Medical Insurance via Vitality, dental cover, and life assurance
  • Enhanced parental leave
  • 1 month paid sabbatical after 4 years at Cleo
  • Regular socials and activities, online and in-person
  • Well pay for your OpenAI subscription
  • Online mental health support via Spill
  • Workplace Nursery Scheme
  • And many more!
  • We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio-economic backgrounds.If there’s anything we can do to accommodate your specific situation, please let us know.

J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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 Winning Cover Letter for Machine Learning Jobs: Proven 4-Paragraph Structure

Learn how to craft the perfect cover letter for machine learning jobs with this proven 4-paragraph structure. Ideal for entry-level candidates, career switchers, and professionals looking to advance in the machine learning sector. When applying for a machine learning job, your cover letter is a vital part of your application. Machine learning is an exciting and rapidly evolving field, and your cover letter offers the chance to demonstrate your technical expertise, passion for AI, and your ability to apply machine learning techniques to solve real-world problems. Writing a cover letter for machine learning roles may feel intimidating, but by following a clear structure, you can showcase your strengths effectively. Whether you're just entering the field, transitioning from another role, or looking to advance your career in machine learning, this article will guide you through a proven four-paragraph structure. We’ll provide practical tips and sample lines to help you create a compelling cover letter that catches the attention of hiring managers in the machine learning job market.

Veterans in Machine Learning: A Military‑to‑Civilian Pathway into AI Careers

Introduction Artificial intelligence is no longer relegated to sci‑fi films—it underpins battlefield decision‑support, fraud detection, and even supermarket logistics. The UK Government’s 2025 AI Sector Deal forecasts an additional £200 billion in GDP by 2030, with machine‑learning (ML) engineers cited as the nation’s second most in‑demand tech role (Tech Nation 2024). The Ministry of Defence’s Defence AI Strategy echoes that urgency, earmarking £1.6 billion for FY 2025–28 to embed ML into planning, logistics, and autonomous systems. If you have ever tuned a radar filter, plotted artillery trajectories, or sifted sensor data for actionable intel, you have already worked with statistical modelling—the backbone of machine learning. This guide shows UK veterans how to reframe military experience for ML roles, leverage MoD transition funding, and land high‑impact positions building the models shaping tomorrow’s defence and commercial landscapes. Quick Win: Bookmark our live board for Machine‑Learning Engineer roles to see who’s hiring today.

Rural-Remote Machine Learning Jobs: Finding Balance Beyond the Big Cities

Over the past decade, machine learning (ML) has transformed from a niche research domain into a pervasive technology underpinning everything from recommendation systems and voice assistants to financial forecasting and autonomous vehicles. Historically, the UK’s major tech hubs—particularly London—have been magnets for top ML talent and corporate headquarters. However, remote work has become mainstream, and many ML professionals are realising they can excel in their field while living far beyond the city limits. At MachineLearningJobs.co.uk, we’ve observed a growing interest in positions that allow for a rural lifestyle or a coastal environment, often reflected in search terms like “ML remote countryside” or “tech jobs by the sea.” This surge is no coincidence. Flexible work policies, better rural broadband, and the nature of machine learning tasks—much of which can be done through cloud platforms—are bringing new opportunities to those who wish to swap urban hustle for fresh air and scenic views. Whether you’re a data scientist, ML engineer, researcher, or product manager, a rural or seaside move could reinvigorate your work-life balance. In this article, we’ll unpack why rural-remote ML jobs are on the rise, how you can navigate the challenges of leaving the city, and what you need to do to thrive in a machine learning career beyond the M25. If you’ve dreamt of looking up from your laptop to rolling fields or ocean waves, keep reading—your rural ML role might be closer than you think.