AI Career Spotlight: James Leoni
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 James Leoni, Head of Machine Learning at Papercup, on a mission to build the world’s most expressive synthetic voices.
“Chart your own course — listen to the experience of others but ultimately you are the steward of your own career progression”
James’ career started in theoretical Physics, before moving into machine learning and intentionally setting himself on a path towards leadership and his role today.
Tell us a bit about your job
I am the Head of Machine Learning at an AI dubbing start-up called Papercup. I lead a team of primarily research engineers and MLOps engineers whose mission is to build the world’s most expressive synthetic voices. My days can be incredibly varied because at a startup everyone wears many hats but this is especially true of leaders at my level. Some days I’m down at a lower level with the team helping to design the next set of research experiments, other days I may be focused on coaching, hiring plans, and interview processes to support individuals in the team in their career progression and to ensure we bring the right skillsets into the team as we grow, and still others I’m working with the co-founder/CEO and the rest of the senior leadership team to set technical direction and influence overall business strategy. Most days it’s a combination of all of those things and more!
How did you get into the field of AI? What excites you about working in AI?
I was in the middle of a PhD in theoretical physics when I decided that an academic career path wasn’t for me. Many of my peers who had moved into industry were going into the machine learning/data science field — so it seemed like a natural domain for me! I joined a fintech startup in Cambridge as a junior data scientist after taking a few only machine learning courses on my own. The rest of the skillset I was able to pick up on the job.
Can you talk about some of the career choices you’ve made along the way?
After working for a few years as a machine learning practitioner I knew that I wanted to continue growing my career in this field but move myself towards leadership rather than remain as an individual contributor. I took a very intentional decision to push my career in a direction that would help me build the skills I needed. I joined Amazon Web Services for three main reasons: to get the ‘Big Tech’ experience, to become a machine learning generalist rather than specialised into one domain, and to learn how to be an effective people leader.
How did you develop the leadership skills you need for your role?
Gaining a solid sense of self-awareness of where my development areas were was my first step. Moving into leadership requires you have skills in entirely new areas and it takes time to build up a foundation. I looked at job specs for roles similar to the one I have now and compared the description to my background at the time, identifying gaps like people management and setting strategy. I even applied for a few roles before I felt I was fully qualified for them because getting feedback through the application and interview process was very helpful to gauge how much progress I was making and where I still needed to improve. I actively sought out a network of mentors both within and outside of Amazon who I learned a lot from as role models of my possible career trajectories.
Now that I’ve achieved my short term career goal of leading a machine learning research team, personal development has become a very individual exercise. I find having a career coach to be helpful to identify where I want to go next and to work on my development areas.
What’s your best piece of advice for anyone early on in their AI career?
Chart your own course — listen to the experience of others but ultimately you are the steward of your own career progression. There are so many different ways someone can be successful in this field and the number of opportunities continue to grow month on month, year on year. Try out different things, find out what makes you happy/motivated/inspired and continue following that path.
What are you excited for in the future of AI?
It’s obviously a very exciting time for AI right now — it’s moving at a much faster clip compared to when I first got started in it. It’s hard for me to say something pithy about the future, other than the fact that I think it’s still very early days for the field. Deep learning started to gain traction only a decade or so ago and we’re less than three years out from the launch of ChatGPT which ignited the big revolution we find ourselves in now. There’s an unfathomable amount of new technologies, paradigms, products and even entire industries that will be borne out of AI that we haven’t even started to think about yet.
