AI Career Spotlight: Maria Mestre
Artificial intelligence is shaping the future, but what do real careers in AI actually look like?
In this career spotlight series, we showcase the career paths, daily work, and impact of people working in AI. Whether you’re an aspiring researcher, an engineer, or simply interested in AI, these stories will give you a firsthand look at the possibilities ahead of you.
Today, we speak with Maria Rosario Mestre, a Senior AI Engineer at Taylor and Francis Group. Her career has taken her from signal processing to NLP, in science and product roles.
“Careers are a long game, and sometimes you cannot connect the dots until much later”
Maria obtained her PhD from Cambridge University, and has since worked in industry.
Tell us a bit about your job
I am part of the R&D team of academic publisher Taylor & Francis. My team is responsible for developing prototypes using the latest technologies to solve business problems. Business problems can range from facilitating the job of our editors, or making content discovery more seamless for our users. I am working on a project around accessibility at the moment, which is very exciting since it’s a way of putting technology to good use. I spend most of the day writing code, making sense of data and I also have to liaise with other roles within the business to understand their requirements.
How did you get into the field of AI? What excites you about working in AI?
After doing a PhD, I moved into industry where I worked as a data scientist for many years, mostly specialising in natural language processing. Initially, it was all about “big data” where we ran predictive algorithms on huge Spark clusters. I started looking into deep learning first where we had to build content classifiers and LSTMs were all the rage. The biggest shift with AI is we can now solve problems that were not accessible to most companies before. The way we create and consume content will change completely, and AI will be an ingrained parts of our days, touching all aspects of our lives.
Tell us about a career choice you’ve made along the way
I worked as a technical product manager for a few years before going back to a more technical career path recently. Choosing one or the other has never been a straightforward choice for me, but I’m happy where I am now.
How did you develop the leadership skills you need for your role?
My role has two important components: awareness of the latest technologies, and the ability to estimate effort to solve a specific problem. For the former, there are a lot of resources out there to stay up-to-date technically, like technical newsletters, podcasts or social media. I have honed my LinlkedIn network so I get a lot of useful technical posts in my newsfeed. Estimating effort is a harder skill to get, but coding on your own projects, or even running the tutorials in technical docs can be very helpful for that.
What’s your best piece of advice for anyone early on in their AI career?
I think this advice applies to any career path: careers are a long game, and sometimes you cannot connect the dots until much later. At the beginning of your career, make sure you pick a good team with senior people who can support you and help you grow, and work on what interests you!
What are you excited for in the future of AI?
I’m mostly excited to see what impact AI will have on publishing and the consumption of academic knowledge. We’re at a crucial point where there’s an increasing amount of AI content being produced, while at the same time people will be able to consume more content than before. It makes my head spin when I think of it!
