My path to machine learning at Cleo

I love the word engineer. I've always had the greatest admiration for this discipline. I thought, sure, scientists come up with theories, but engineers are the ones who make stuff work.

Yet for a very long time, it didn't occur to me that the word could ever apply to me. I loved maths, yes, but then I loved everything, really, and at school, being good at maths was already very much touted as a boy thing.

I still attended after-hours advanced maths classes for fun, even though they weren't my official electives (i.e., ancient Greek and history—true story). But my quantitative proclivities waited many more years to be satisfied.

Paths to maths and joining Cleo

My return to maths was a very roundabout, slow process. I did a year of business school, then completed a bachelor's degree in French and Spanish literature, a master's in linguistics and finally a PhD in psycholinguistics.

With moving into linguistics and psychology came more objectivity and learning about statistical methods and programming. However, I did not forget the love of language that brought me to the field in the first place. Discovering natural language processing (NLP) was a pivotal point: my two loves, languages and maths, coming together!

After my PhD, I ping-ponged between industry and academia for a while before being swept up by the fierce, fast-moving wave that is Cleo.

Cleo-ing in style

When I first started at Cleo, I was expecting my first child. Some companies would have been put off by this, but not Cleo. They welcomed me warmly and were incredibly generous and supportive. It's not easy combining parenting with fast-paced fields like tech and AI, but Cleo gives you the space and support you need to thrive.

My onboarding experience at Cleo was one of the best I've had. While there are many things to get up to speed with at a fast-growing company constantly striving to upgrade user experience, you are given appropriate time and mentorship to find your flow.

I would say Cleo strikes the perfect balance between structure and independence for an ML engineer. There are robust frameworks in place to ensure that work is aligned with company and business goals, milestones are explicitly set, and regular communication is scheduled to maximize visibility. However, Cleo is not a company set in its ways or unwilling to listen to those outside of senior leadership. Ways of working and product initiatives are constantly assessed and promptly revised if found lacking, creating a cycle of quick, positive iteration and continuous improvement.

ML engineers also own their work end to end at Cleo. That means they are responsible for bringing projects all the way from idea to production and in-app use. This is carried out through collaboration and brainstorming within squads: topic-focused units where a team of product analysts, front-end engineers, back-end engineers, content designers, and ML engineers come together under a product manager to deliver features. This guarantees that there isn't a siloing of data science work where half-baked ideas are handed over without follow-up. However, Cleo benefits from a robust ops pipeline and team who are always happy to provide help.

Cleo's culture is one of inclusivity (the true, nontoken kind) and openness. The latter is manifested, among other things, in Cleo's complete transparency regarding salary bands and criteria for advancement. The former is experienced every day when looking around and witnessing a high diversity of genders, ethnicities, and backgrounds. I don't feel out of place because I am a linguist. On the contrary, I feel like my perspective, like that of everyone in the company, is valued, and its out-of-the-boxness embraced.