Lead MLOps Engineer

Most money apps talk at you. Cleo talks back

We're not building another finance app. We're building the world's first AI financial assistant, one that actually understands your money and makes you better at it, and we're changing the world's relationship with money in the process For everyone, whatever their background or balance.

The proof: profitable, fast-growing, a unicorn with over $300M in ARR, and millions of people who now feel differently about their money.

We love original thinkers who challenge the status quo. We move fast, tell the truth, and there's nowhere to hide from good work here. If that excites you more than it scares you, you'll fit right in.

Follow us on LinkedIn for new features and the occasional roast

Meet the team

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Here at Cleo, we have ~200 Engineers organised into Pillars. Our stack spans a Ruby on Rails monolith, a single React Native (TypeScript) app frontend, Python for machine learning services, and PostgreSQL, all hosted on AWS and shipped through Kubernetes - backend goes out multiple times a week, app releases to Google and Apple at least weekly. To thrive here, our engineers proactively use our Engineering Principles to guide daily decisions, take ownership of problems beyond their own squad to drive broader impact, and actively drive their own learning, development, and growth.

What you’ll be doing

The right candidate will support our product teams in achieving their OKRs while championing best practices in data engineering and MLOps. In this role, you'll work closely with product teams to ensure they effectively adopt the tools, frameworks, and processes provided by the Data Platform team, enabling them to build scalable, efficient, and reliable data and ML solutions. You'll help teams implement robust data pipelines, model deployment workflows, monitoring strategies, and cost-efficient practices to improve their data-driven capabilities.

At the same time, you'll act as a crucial bridge between product teams and the Data Platform team, gathering insights on real-world challenges, gaps, and pain points in the existing platform. By surfacing these issues and collaborating with the platform team, you'll contribute to the continuous improvement of our internal tooling and infrastructure, ensuring it better serves the needs of our engineers and data scientists. This is an opportunity to blend hands-on engineering with strategic impact, influencing both product success and the evolution of our data platform.

About you

You are passionate about making a positive difference in society by improving the financial health of our users. You align with our company values and engineering principles, which drive our ways of working and software delivery.

Our ideal candidate will have experience in

• Data System design and breaking down work
• Solid experience with data eng language (python ideal)
• Knowledge of at least one distributed processing framework. Plus if its streaming (eg: PySpark, Flink)
• Containerisation \& orchestration; Docker, Kubernetes
• Infrastructure as Code; Terraform
• Software engineering and best practises - proficiency in Python (preferred), code quality and maintainability.
• Good knowledge of different storage types and when to use. OLTP, OLAP, S3
• Understanding value and product thinking
• Experience working cross-functionally; Ability to work with data scientists, software engineers, and product managers to align ML initiatives with business goals.

Nice to haves

• Experience running streaming platform and knowledge of stream > table and table > stream
• Deep technical knowledge of core data structures, distributed processing. Practical application >> theoretical knowledge. It important to have aptitude to understand concepts, but application and reasoning over value is more important
• Monitoring and alerting how it pertains to data system -> also this is important, not strictly necessary as something that can be learnt
• Deploying APIs and systems outside of core data platform -> moving more into the ability to deploy ML systems. Happy if this person has not done this, but willing to learn
• Experience working with Feature Stores
• Experience building and managing ML pipelines e.g: Kubeflow, MLflow, Airflow, Flyte.

Our Tech Stack

Cleo is built as a Ruby on Rails monolith with a single React Native app frontend, utilising TypeScript. We also leverage Python for machine learning services and PostgreSQL for our database, all hosted on AWS. Our CI/CD pipeline is fully automated, with production deployments happening on every merge via Heroku. Our backend engineers deploy multiple times a week, and we release our frontend app to Google and Apple for review at least once a week.

While we take a pragmatic approach, we place a strong emphasis on quality. Our code is peer-reviewed, and we maintain automated testing using Minitest and CircleCI. We're also actively working towards a more modular architecture, focusing on separating concerns to achieve all the benefits of microservices within a monolith, while progressively refactoring our code as we build new features. Everyone in the engineering team contributes to driving our technical strategy, voices \& ideas from all levels are valued: we are all owners at Cleo.

Interview Process

• Recruiter Interview (30 mins)
• Whiteboard Interview (60 mins)
• Technical Interview (60 mins)
• Final Interview (45 mins)

What we offer

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We offer benefits package built to support you in and out of work. Benefits vary by country and include meaningful equity, comprehensive health, dental and vision insurance, mental health support, a paid one-month sabbatical after four years, a learning and development platform, pension or retirement contributions, and location-specific leave entitlements.

For the full breakdown on what's available in your location, visit our Candidate Hub

What Matters

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Cleo's mission is to change the world's relationship with money. We can't do that without building a brilliant, genuinely diverse team - and making sure our hiring process gives everyone a fair shot, running a fair and transparent recruitment process in which every candidate is considered on their metris.

We're glad to make reasonable adjustments at any stage of the process. If there's anything we can do to support you in showing us your best, please let your recruiter or one of the team know.

We may use AI-assisted tools during the recruitment process to take support the team in different ways like taking notes or referring back to conversations. They do not replace the team and all applications are reviewed by a Cleo employee thoroughly!

Compensation Range: £87.4K - £145K