Machine Learning Engineer - Personalisation

Cleo; London; On-site; GBP 50,000 - 90,000

Job description

About Cleo

At Cleo, we're not just building another fintech app. We're embarking on a mission to fundamentally change humanity's relationship with money. Imagine a world where everyone, regardless of background or income, has access to a hyper-intelligent financial advisor in their pocket. That's the future we're 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 isn't just a job; it's 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.

About the role

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 world's 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

1. 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.
2. Deploying these models into our production environments using our in-house ML platform, which you can read about on our blog: Let's have an Espresso: MLOps at Cleo.
3. Integrating LLMs hosted by OpenAI, Anthropic, GCP, AWS.
4. Working cross-functionally with backend engineers, data analysts, UX writers, product managers, and others to ship features that improve our users' financial health.
5. Driving the adoption of appropriate state-of-the-art techniques for recommendation, message campaign optimisation, and contextual bandits.
6. Communicating the team's successes and learnings at the company level & beyond.
7. 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.
8. 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:

1. Designed and implemented AI agents to analyse and extract insights from users' transactional data.
2. Developed models to interpret transactional data, enhancing the understanding of users' finances.
3. Created contextual intent classifiers to understand user conversations with Cleo, enabling tailored and accurate platform responses.
4. Engineered ML models to identify and deliver relevant actions to users within Cleo, ensuring a seamless, context-aware conversational experience.
5. Built models to evaluate risk in customer interactions with bank transaction features and user activities.
6. Developed optimisation models to improve payment success rates for customers while minimising business costs.

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

1. 3-5 years of experience in data science, machine learning engineering, or related roles.
2. Excellent knowledge of both Data Science (python, SQL) and production tools.
3. Strong ability to communicate findings to non-technical stakeholders.
4. Experience deploying machine learning models into production; familiarity with Docker containers and container orchestration tools is a plus.

Nice to have

1. Experience with recommender systems, personalisation, or ad optimisation.

What do you get for all your hard work?

1. A competitive compensation package (base + equity) with bi-annual reviews, aligned to our quarterly OKR planning cycles.
2. Work at one of the fastest-growing tech startups, backed by top VC firms, Balderton & EQT Ventures.
3. A clear progression plan.
4. Flexibility.
5. Work where you work best.
6. 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.
• We'll 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.