AI Career Spotlight: Neal Lathia

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 Neal Lathia, CTO at Gradient Labs . Neal’s career is guided by curiosity, reflection, and a belief in building meaningful, real-world AI with and for people.

“For folks starting out, I think it’s worth knowing that there isn’t one well-forged AI career path anymore. The field, its job titles, and its applications have diversified.”

Read how Neal’s journey through academia, freelancing & tech organisations led him to co-found a startup.

Tell us a bit about your job

I’m the co-founder CTO of Gradient Labs; we’re an 18 month old London startup that is building an AI agent for high-stakes customer support. In a small startup, there’s a bit of everything to do — building the team, the platform, the product, all the way through to talking to customers. I’m currently working closely with 5 Engineers every day, with projects that focus on supercharging what our AI agent can do, alongside our ability to build it safely. I’m incredibly proud of this team, and all the customers that have joined us on this journey!

How did you get into the field of AI? What excites you about working in AI?

I discovered AI during my PhD in Computer Science. At the time, I was fascinated by early recommendation products (particularly for music) and was drawn into the world of recommender systems when Netflix announced their $1M prize. One of the areas that I researched focused on differences between academic methods for ML (showing that a model was x% better) and the lived experience of people using ML systems over time. I think that is what excited me to want to build ‘real’ ML systems.

Can you talk about some of the career choices you’ve made along the way?

My career has taken some odd turns — academia, freelancing, large tech, UK scale-up and, now, a startup. Every step of a career does have a lot of trade-offs: time, money, opportunity, how interesting the work is, who you are working with, and so on. It’s not easy to navigate, and know whether you are making the right choice! Oftentimes, this clarity only comes with hindsight.

Because of that, one approach that I use is to try and imagine whether my future self will regret a decision that I am making now. It usually helps me to gain some perspective.

How did you develop the leadership skills you need for your role?

Early on in my career I was fortunate to have some colleagues and mentors who I admired and learned from- my PhD advisors, internship mentor, and other folks who kindly gave me their time and wisdom.

Later on in my career, I was lucky to be working in companies that had great tech, product, and company leaders, but had less direct access to them. One tip that I picked up along the way was to ask: when I see a person with ‘great’ leadership skills, why do I think that they are great? (the opposite question is also valid!). This really helped me to hone my personal understanding of leadership.

What’s your best piece of advice for anyone early on in their AI career?

When I was starting out, AI (ML) was very much its own thing- there was a clear distinction between the people who worked on it, who typically had an academic background, and everyone else. AI is now becoming a ubiquitous technology: it’s everywhere.

For folks starting out, I think it’s worth knowing that there isn’t one well-forged AI career path anymore. The field, its job titles, and its applications have diversified. Indeed, everyone at Gradient Labs has a career “in AI” right now- even though they may be working on platform, product, operations, or design.

What are you excited for in the future of AI?

I do believe that people, decades or centuries from now, will look back on these times as a pivotal moment for AI. It’s wonderful and exciting to think that the forward-looking opportunity for AI, right now, is larger than anything that has been built so far.