MLOps Engineer
Indexed description
Cleo is a rare success story: a profitable, fast-growing unicorn with over $300 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.
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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.
- 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.
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.
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.
- 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. Our Poland team works fully remotely, but we host virtual socials and an annual company offsite somewhere in Europe with all expenses paid.
- 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)
- Private medical insurance with Alan
- 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
- We’ll be employing you through our EOR provider, Deel, and can discuss our current benefit offering directly.
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