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.
Follow us on LinkedIn to keep up to date with new product features and insights from the team.

ABOUT THE ROLE

We’re hiring a Data Engineer / MLOps Engineer to support Cleo’s product teams in delivering impactful data-driven solutions. In this role, you’ll help teams adopt our internal Data Platform, build efficient data pipelines, and deploy machine learning models effectively at scale. You’ll serve as a bridge between product teams and our Data Platform team, ensuring tools and infrastructure meet real-world needs and continuously evolve.
You’ll combine hands-on technical work with strategic collaboration, directly influencing how Cleo leverages data to achieve product and business goals.

WHAT YOU’LL DO:

• Collaborate closely with product teams to implement robust, scalable data pipelines and ML workflows.
• Guide teams in adopting best practices around data engineering, infrastructure management, and MLOps.
• Surface practical insights from product teams to inform improvements in our internal Data Platform.
• Contribute actively to enhancing our data and ML infrastructure—focusing on usability, efficiency, reliability, and cost-effectiveness.
• Mentor and support engineers and data scientists in data engineering and MLOps best practices.