AI Career Spotlight: Tom Diethe

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 Tom Diethe, Head of the Centre for AI at AstraZeneca. By combining scientific curiosity, technical expertise, and leadership, Tom’s excited to harness AI’s potential to revolutionize healthcare and beyond.

“It’s an amazing area to work in, as we’re on the brink of having agentic AI that can really transform the way we develop drugs to have life changing impacts for our patients.”

Tom’s journey has taken him through diverse sectors like defence and pharma, and settings like Big Tech and academia, to today’s work in an interdisciplinary & collaborative setting.

Tell us a bit about your job

I head up our Centre for AI at AstraZeneca, based in the Biopharma R&D division. We’re a team of AI Scientists and AI Engineers working on a variety of projects across both discovery and development. My own role is a mixture between being a scientist (staying as close as possible to the technical aspects of our projects), a department head, an AI leader within the company, and representing AstraZeneca in the AI community.

Some exciting projects include using AI guided protein for antibodies and other biologics, computer vision applied to many imaging modalities (Echo, Endoscopy, CT, MRI, Retinal …), understanding of mechanisms of action from spatial transcriptomics data, and LLMs for optimising clinical trial documentation. We’re proud to say that we have AI that is designing and screening molecules happening right now, and we’re embedding AI into ways of working across the whole R&D organisation.

It’s an amazing area to work in, as we’re on the brink of having agentic AI that can really transform the way we develop drugs to have life changing impacts for our patients. I’m fortunate to work with amazing specialists in many different areas — medicine, biology, chemistry, regulatory, commercial to name a few — who all come together in a really collaborative setting.

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

I was first interested in AI as a teenager after reading “Gödel, Escher, Bach: an Eternal Golden Braid” by Douglas Hofstadter. At that time there wasn’t an obvious route in, so I started by studying Psychology (human intelligence), where I focussed on cognitive science and neuroscience. After graduating I spent a few years in the defence sector working on the “Cognitive Cockpit” project — an early attempt at AI for pilots, the time was right to go back to study again. I first took the MSc in Intelligent Systems at UCL, and after that I was lucky enough to get a place on the PhD programme also at UCL, supervised by the incredible John Shawe-Taylor.

For many years I considered myself as a Machine Learning person, and honestly got a little frustrated by ML being rebranded as AI (what’s an “AI algorithm”!?). However, nowadays I’m really excited by the fact that real AI is now coming to fruition, and I’m starting to feel comfortable calling myself an AI expert! There may be challenges ahead, but the potential of this technology is incredibly exciting, particularly in applications to healthcare.

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

In my career I’ve worked in defence, the tech industry, academia, and now pharmaceuticals. One of the toughest choices I had was back in 2014. I was working at Microsoft Research in Cambridge, but ultimately not that happy, and felt that I still needed to enhance my academic profile. I took a research fellowship position at the University of Bristol working in ML applied to digital healthcare (the “SPHERE” project), where I spent 3 amazing years working with Peter Flach and others. It was tough, as it was roughly a 50% pay cut (!!), but thankfully I have an incredibly supportive wife who encouraged me to follow my dreams. Working there really unlocked everything else that came later.

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

This might be hard to hear, but there aren’t really shortcuts to this! My first jump into (official) management was when I joined Amazon in 2017, and there I developed from a manager of a small number of individuals to eventually leading a larger team (manager of managers), but that’s only part of the picture. For me leadership is about having domain knowledge in your chosen area of expertise, where the challenge then becomes how to keep yourself up to date, particularly in such a fast moving field as AI, combined with learning techniques such as how to coach people rather than simply manage them (“teach a man to fish …”). It’s not something I ever really thought I’d do early in my career since I was really only passionate about the science, but later I realised that I love the people side of my job too, with all the complications it sometimes brings!

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

Don’t be afraid that others seem to know more than you! I spent a lot of energy on conquering my “imposter syndrome”, but at some point you realise everyone else feels the same way!

What are you excited for in the future of AI?

There are many problems in the world at the moment (climate, geopolitics, health crises), and it can feel sometimes like there is only impending doom and no way out. To me AI is humanity’s way out of this — it has the potential to revolutionise everything. Let’s embrace it for the good of people, society and the planet!

AI Career Spotlight: Simon Fothergill

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 an idea of the possibilities ahead of you.

Today, we speak with Simon Fothergill, Lead AI Engineer. As an Engineer, Simon focuses on building systems that not only work, but also contribute positively — balancing technical depth with responsible leadership, and bringing clarity to the complex.

“Leadership is… taking responsibility for replacing ambiguity with clarity, by uniting people in a common direction at the opportune moment.”

Simon’s journey started with an undergraduate degree project, and since then he’s built his career in the field of natural language processing.

Tell us a bit about your job

I am an engineer because I love to build things and get them working so they positively contribute to people’s lives.

The AI industry is currently like a sports arena full of lego, with new models and bricks being poured in through every door and window, as all sorts of people build, build, build!

Companies currently need AI engineers with:

• Deeply technical, hands-on skill to choose the right bricks to assemble in the right way.

• Technical leadership and product/project/team management skills to navigate this sea of bricks and surf the waves.

• Breadth of experience across applications to cope with the uncertainty of this sea.

Whether I am consulting or in a full-time role, I look for roles with a mix of these opportunities and that would broaden my experiences.

I enjoy working with domain experts who can inspire and refine product definitions and the corresponding AI models.

I develop evaluation frameworks that allow product owners to navigate project iterations.

I specify requirements for fairly and authentically collecting and annotating data, to a suitable level of saturation. This allows me to experiment with safe and transparent product iterations, one subdomain at a time. Building beta systems to collect high quality data as soon as possible can sometimes be necessary.

Design diagrams and code reviews are great ways to improve as a software engineer, so I invest significant time on this.

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

I worked on an AI system for my final year undergraduate project and didn’t want to stop!

AI is the automated prediction of information. I love the magical contradiction of making things happen all by themselves. Predicted information, whether useful knowledge in itself or not, is the reflection of our world through the mirror that is the AI model. The better shaped and polished the mirror is, the more it reveals about the world and the better we can understand and contribute to it. Recently I’ve worked on modelling phenomena ranging from the risk profiles of legal proceedings, to the inspiration of journalism, to the empathy of psychotherapy.

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

I think the level of responsibility one wishes to take in developing AI systems is currently an important factor in career direction.

It takes a village… just because a company has more money, doesn’t necessarily mean more things happen there.

I have ended up on the natural language processing side of things more than the computer vision side, as a matter of chance and have built on that.

I’ve favoured applications over generic platforms as I learnt about parts of the world being modelled. I build ‘vs’ buy, as soon as possible, for responsibility, IP generation, customisation and development speed. I keep models as simple as possible for as long as possible — Occam’s Razor.

I’ve always had the role of a hands-on, full-stack, AI software engineer, albeit with an increase in scope, management and leadership responsibilities. But my community name has changed around me, from “software engineering” to “data science” to “machine learning engineering” to “AI engineering” and even now back to “software engineering”.

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

Leadership is not the management of scope, schedule or ways of working, but taking responsibility for replacing ambiguity with clarity, by uniting people in a common direction at the opportune moment. Justifying the value chain can help, as can humility and serving, protecting, teaching and gently challenging those around you.

I think I’ve reflected on these ideas, which have come from Jesus, parents, 1–1 conversations with mentors (previously highly successful businessmen) and also just common sense.

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

Being clear is more important than being right.

What are you excited for in the future of AI?

The current “efficiency (gold) rush”, towards AI models that we hope reflect our world and automatically predict something of business value, can be worth the risks of inaccuracy, opaqueness and the uncanny valley, especially for non-critical, commonsense or human-in-the-loop applications.

However, exciting opportunities to mitigate risks include:

• Not trying to solve problems that shouldn’t be solved by AI, with AI.

• Not always wanting to listen to an UPF (Ultra Processed Foundation model).

• Current foundation models are not deep enough for some niche domains.

• Judging when the iterative, empirical process of AI development is likely to fail to reveal a suitable model.

• Algorithms do not ‘deserve’ to be trusted in the same way humans deserve to be trusted.

• Over-use of co-pilots can weaken a human user’s abilities to critique output.

AI Career Spotlight: Peter Wooldridge

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 talk with Peter Wooldridge, Director of Machine Learning at Monolith.

My advice to that would be: learn how to solve problems. Problem solving transcends technology and is the key meta-skill that enables adaptability and longevity in, I think, any field.

Peter’s journey into AI began in Software Engineering, where he built a solid foundation before using his maths background to transition towards AI.

Tell us a bit about your job

As Director of Machine Learning at Monolith, I lead three specialised teams: ML Engineering, Forward Deployed, and Research Science. My role is ensuring these teams have what they need to deliver and clear direction on short and medium-term goals.

On a good day, I try to start with a simple plan: “what 1 key thing would I like to have done by the end of the day.” With numerous meetings and 1:1s, this focused approach helps me stay realistic and productive.

Our teams work on distinct challenges: ML Engineering builds models for our SaaS platform, Forward Deployed collaborates with OEM customers to implement machine learning in our customers testing processes, and Research tackles some of industry’s hardest unsolved problems that could become new product features.

One project I’m particularly proud of is our anomaly detection system, which began life as a simple customer experiment but evolved into one of our platform’s flagship features. What started as a scrappy proof-of-concept with a single automotive client now helps engineers across multiple industries instantly pinpoint hidden irregularities across hundreds of data channels.

Beyond day-to-day execution, my role has a strategic element — planning 3–6 months ahead, determining which initiatives to pursue, and establishing success metrics. This involves integrating signals from across the business and market into a coherent strategy.

I work with technical teams, participate in executive meetings, and speak directly with customers. Customer meetings are one of the favorite aspects of my job, as it gives me first hand understanding of their pain points which strongly informs our planning.

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

I was lucky enough to start my career at IBM, joining their software development graduate scheme in 2010. Starting at a company like IBM offered two major advantages: exposure to incredibly varied work, and access to world experts in virtually any technical domain you can probably encounter.

After 3 years working in a very talented team learning the foundations of what it means to build software in the real world, I saw an opportunity to join IBM’s Emerging Technologies Services team. I had only a cursory understanding of AI back then, having taken some courses on SQL and big data. Back then, the industry was buzzing with excitement about big data and distributed computing platforms like Hadoop.

I was drawn to AI because it allowed me to move beyond pure software engineering, which never quite felt like my natural home. It was a great opportunity to fuse my then newly developed software engineering skills with my maths background.

In 2013, I transitioned into a Data Science Consultant role that opened up opportunities to travel all over Europe and work with a variety of companies across industries. I typically collaborated with IBM consulting teams on diverse AI projects, ranging from large-scale ETL pipelines to distributed ML models built on massive datasets. Back then I was coding in a mixture of Python, R and SQL and sometimes Java.

The rapidly evolving nature of AI makes it both an energising and sometimes challenging field to work in. Despite the fact that AI is all about automation and streamlining, the thing that keeps me in the field is actually the people. Being able to work alongside really talented individuals is what makes it really fun for me.

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

One of the big transitions in my career was moving from an IC (individual contributor) into leadership. When I first made the move, it was tough. I felt as though my coding was my currency and I was holding on to it tightly. In the initial stages of management, I was trying to juggle both coding and leading. This can work for a bit, but as the teams scale, juggling the two led to doing neither particularly well, and so I had to choose. Once I’d made peace with the transition, I felt liberated to focus on leadership.

One of the most valuable things I’ve learned in leadership is how to handle unclear, vague requests. Initially, I used to get frustrated by this lack of clarity. But I discovered that true leadership is about seeking clarity yourself and playing it back to help others. I’ve found this is best addressed by asking better questions and reflecting people’s thoughts back to them to uncover what’s really in their head. This approach transformed not just how I led, but how I solved problems entirely.

I believe the higher-level perspective gained in leadership also makes me a better developer, when I do code. Understanding the “why” behind technical decisions and having the confidence to seek clarity when requirements are vague are skills that would enhance any technical work I do. The best technical solutions come from understanding the problem deeply rather than just implementing what’s asked for.

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

I don’t think I’ve ever really done any formal training on this. I like to observe people who I see as strong leaders and try to understand what it is that is resonating with me.

Something I admire in strong leaders is their ability to communicate clearly. This can be from little details like the way Steve Jobs uses pauses for effect on stage. I think communication is such an important skill to develop as a leader in tech, especially the communication of technical concepts to non-technical audiences.

Communication skills can definitely be learned and developed. I’ve found the most effective method is simply recording yourself (quite easy these days with lots of recorded meetings) and noting down what you need to work on. Alternatively, share the recording with someone who’s a good communicator to give you brutally honest feedback. It’s painful in the beginning but there might be certain ticks or things you do that only others can spot, and it’s worth finding these out.

I think the other thing about leading, specifically in a startup ecosystem, is learning to make decisions under uncertainty. The growth of the company is directly correlated with the speed with which decisions are made. Very rarely are choices black and white, but the worst of both worlds is usually trying to hedge your bets and half-pursue two options, which results in mediocre results across the board. Deciding at a leadership level is often about clarifying all the things you won’t be doing in order to stay on track to your goals. Being able to exclude things is a highly valuable way to make a team more effective.

I’m not sure if there’s a playbook for how to improve this — it comes from experience, but I think decisiveness can start with really small behavioral patterns. For example, when someone asks “where do you want to go for dinner tonight?”. We have agency in how we respond. We can say “I don’t mind, what do you feel like?” or, we can make a proposal: “let’s go to this Italian place.” These small behavioral patterns are what eventually translate to the bigger things.

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

One fundamental challenge people have today more than ever I think is how to stay relevant in a rapidly changing field like AI. How to keep being useful with the pace at which AI is evolving?

My advice to that would be: learn how to solve problems. Problem solving transcends technology and is the key meta-skill that enables adaptability and longevity in, I think, any field.

That doesn’t mean you shouldn’t learn Python, but, thinking about problems from first principles means that if/when programming language X falls out of fashion, you are able to be agile and adapt.

From a programming perspective, this looks like applying algorithmic thinking or data structure knowledge (arrays, trees, graphs, hash tables etc.) to problems rather than obsessing over a specific syntax. This is where I’ve seen experienced developers able to switch programming languages super quickly — they’re not starting from scratch because they’re able to apply universal problem-solving principles, just with a different syntax.

This principle extends beyond just programming languages to the field of AI itself. For example, I’d say it’s probably more valuable to learn the key linear algebra operations (matrix multiplication, eigenvectors, vector spaces, etc.) that basically underpin every modern ML algorithm than it is to learn the specific architecture behind transformer networks. The former has a greater return on investment since it can aid understanding of basically any modern ML algorithm.

What are you excited for in the future of AI?

2025 is going to be the year of agentic applications. Agentic AI systems are those that can plan, reason, and take actions to accomplish specific goals with minimal human oversight. I’m expecting to see people creating agents for things that don’t really need them, but amidst the hype, I believe we’ll witness some truly transformative applications.

What excites me most is how these technologies will reshape knowledge work. For instance, in engineering testing — my field — I envision AI agents that can autonomously design experiments, analyse results, identify anomalies, and suggest next tests. This could compress months of testing cycles into days, fundamentally changing the revolutionising the product development workflow.

Another big breakthrough will come as lightweight model architectures enable more on-device intelligence. Running sophisticated models locally addresses many privacy concerns that currently limit adoption in sensitive industries. We’re starting to see this with models like ollama that can run on laptops, opening possibilities for AI applications in healthcare, finance, and other regulated sectors.

That said, there’s still significant work needed to make agentic apps reliable in production environments. The gap between impressive demos and reliable business applications is substantial. The teams that can build robust systems, align them with business processes, and create effective human-AI collaboration will be the ones delivering value beyond the initial hype.

AI Career Spotlight: Jo Stansfield

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 Jo Stansfield, who founded her own business Inclusioneering. As part of that business, she spends time working on Responsible AI — exploring and mitigating the risks of technology.

“Retrospectively, I can tell a story about a career path that seems logical and like a natural flow. But as you make each move, each one feels like a step into the unknown.”

Jo’s career started in Software Engineering, and over time she’s moved towards working on the human side of technology.

Tell us a bit about your job

I set up my business, Inclusioneering, in 2021. I believe technology should be created by and for everyone, but sadly we are far from that situation today. Inclusioneering gives me a way to work towards that goal, with some awesome associates who work with me.

My job is very varied. As a micro-business owner I do just about everything you can imagine! My favourite work, and the reason I do it, is supporting clients to embed more inclusive, equitable practices into the way they work, and design for positive social impact of their innovations. Second is what I call inclusive innovation, which is working with innovative teams to build greater inclusion and psychological safety. And third is Responsible Tech/AI. This is about exploring the risks of the technology being developed or deployed, to establish good governance and manage the negative impacts, to strive for the most beneficial and fair outcomes for everybody.

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

Before working in my current role, I worked for about 20 years as a software engineer, then leader, building industrial software. That spans a lot of things, but a focus for me was on products that manage the enormous amounts of data associated with huge scale engineering projects, like building a ship or a power plant.

What really got me into the field of AI though, was when I pivoted my career path to focus on the human side of tech/engineering. When the COVID pandemic hit in 2020, I followed the news stories about GCSE and A-level grades being determined by algorithm with horror. It was immediately apparent to me how discriminatory that would be, and that an algorithm that essentially pulls everyone to the average of what it determines “people like them” achieve would be immensely harmful.

I signed up to work with a charity called ForHumanity, that builds audit schemes of AI systems. I got really stuck in and was one of the first people to write audit criteria, examining risks of systems but just as importantly also the governance, oversight and accountability of them. I love my work in AI, because it combines my passion for technology with my passion for people. With AI, those things are inherently linked. We are creating complex, sociotechnical systems. Determining what that means and how to manage it fascinates and excites me.

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

Retrospectively, I can tell a story about a career path that seems logical and like a natural flow. But as you make each move, each one feels like a step into the unknown. A lot of things are emergent. I began my career working as a software engineer. Over time my role transitioned to become a product owner and product manager. That worked for me in that I was closer to the engineering customer side, but it was hard to lose the technical detail in my role.

When I started a family I decided to work part time (4 days a week) on return from maternity leave. I’ll spare the full story here, but essentially that experience opened my eyes to the systemic challenges facing anyone who doesn’t fit the mould in tech/engineering lines of work. I had a tough time getting back to work, further complicated by office politics. But I found an ally in our CTO and as a side-role I started helping HR with data analysis about our organisation. It sparked a series of actions and change, which was amazing.

My interest was growing in what I now call the human-side of engineering — as a leader, and also from the inclusion point of view — and I signed up to take an MSc in Organisational Psychology part time alongside my work. Inevitably, I’d taken on way too much, and I couldn’t do justice to any of my many roles, including as a mum. I used a tool I’d learned about in the coaching module of my course — a life balance wheel — and it helped me get clear on what was most important to me. I made the hugely difficult decision to leave my technical role, and I approached HR to ask if the side-role I was doing could become an actual job, with part-time hours.

Astonishingly, they agreed. I became the first person to lead diversity and inclusion in the organisation, and moved into HR. Roll forward a couple of years later, and I completed my MSc. I suddenly had free time on my hands, and I knew I wanted to make broader change to industry, not only a single company. I’d focused my MSc towards diversity and inclusion in tech, and wanted to use it. Again I approached HR, and asked if I could use my now-free time to start a consulting business alongside my work there. Again, astonishingly, they agreed. I set up Inclusioneering and started my independent consultancy.

That takes my story up to how I got into AI, and was happy to find a path that reunited my technical and EDI experience. Almost 2 years ago, in 2023, I left my HR role and went full time in my business. It’s been an exciting and terrifying journey. The rollercoaster of running a small business is real, but I have no regrets.

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

Becoming a people leader was a huge transition for me. It came amidst quite a lot of turmoil at work, so there was that extra challenge. I’d been a product manager for several years, but leading a whole department was a big step up.

I was fortunate to be given a place on a women’s leadership programme, which taught me a lot of new skills and gave me a new perspective on leadership. Having worked almost predominantly with men, and going over 15 years with no women in my management chain, I was very curious about how a successful woman leader may look. Would there even be a difference? The other women on the course were fantastic, and from a range of different backgrounds, and at different levels of seniority. To my surprise, I did find that seeing how they approached their leadership resonated with me in a way that I’d not found before with my male colleagues. While I admired and learned a lot from many of them, it was somehow easier to “try on” the ways the women leaders did it, and see what fitted for me.

I also had some sessions with a coach, who really helped me to unlock how I was going to lead my teams. It was very empowering, and helped me gain real clarity and focus.

What are you excited for in the future of AI?

I am fascinated by the field of AI ethics and responsible AI. It exists because there are, of course, significant risks from the proliferation of AI. But I love working at that interplay between people and systems, and it’s a rewarding area to work in because it’s so important.

The technical application that most excites me most about AI is its potential to help us find solutions to the climate crisis. I’ve done lots of work now with engineering teams who are innovating more sustainable approaches to industry. AI is already helping with improving efficiency and safety, and has the potential for much more revolutionary changes to how materials are made to cut carbon emissions. Of course, AI comes with its own climate challenges, so there’s work needed to address that so we really get the full benefits.

AI Career Spotlight: Alessandra Tosi

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 Alessandra Tosi, Senior Scientist at Mind Foundry. She‘s also a board member of Women in Machine Learning.

“The beauty of the AI field lies in its variety and the multiple perspectives you can bring to a problem”

Alessandra’s journey started in academic research, before she moved into industry.

Tell us a bit about your job

I am a Senior Scientist at Mind Foundry, specializing in AI deployment within the insurance and infrastructure sectors. My role involves diverse skills, understanding user challenges, researching the most effective scientific approaches to address these issues, designing experiments, analyzing data, and collaborating with cross-functional teams to integrate AI solutions into our products. This requires both scientific depth and breadth, and it’s crucial for me to stay updated with the latest technological developments to provide the best and most innovative solutions to our customers. Recently, I’ve been involved in projects aimed at enhancing road safety, which have a direct impact on both road users and the broader population. I take great pride in delivering innovative solutions that positively affect people’s lives.

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

My journey into AI began during my masters studies in mathematics, where I had the chance to select elective courses in computational mathematics and I grasped how machine learning could be used to solve complex problems. I pursued a PhD with a focus on AI model interpretability and probabilistic geometry, which allowed me to delve deeper into the field and work on cutting-edge research projects.

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

One significant career choice I made was transitioning from academia to industry. While I enjoyed the theoretical aspects of AI research (and my love for math and theory still stands), I wanted to see my work applied in practical settings and have a tangible impact on people’s lives. I moved from an academic position to a startup, joining Mind Foundry from its inception. Contributing to the organization’s evolution and growth has been a tremendous learning experience. It has been incredibly rewarding to see my contributions directly impact products and services used by many people.

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

Developing leadership skills has been a gradual journey for me. I began by leading small projects, progressively taking on larger and more complex initiatives. Mentorship played an important role in my growth, I actively sought guidance from more experienced leaders and continuously incorporated feedback to refine my own approach. Over time, I have supervised interns, managed direct reports, and prioritized their professional development while emphasizing effective communication and fostering a collaborative team environment.

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

A solid technical background in mathematics was incredibly helpful for many aspects of my research, but I also had to work hard to fill the gaps in other areas. To junior members approaching AI, I want to emphasize that you don’t need to know everything from the start. You’ll need to learn aspects outside your expertise as you go. The beauty of the AI field lies in its variety and the multiple perspectives you can bring to a problem. So, don’t be afraid to dive into the field just because you don’t know it all at the beginning. You will learn, and your unique perspective will be a valuable addition to the team.

Also, seek out mentors and build a strong professional network. Learning from others’ experiences can provide valuable insights and open up new opportunities.

What are you excited for in the future of AI?

I’m excited about the potential for AI to address global challenges, such as climate change and resource management. In previous work, I have been investigating how to balance the use of computational resources while enhancing model performance, in order to balance performance and sustainability of AI solutions. AI can provide innovative solutions for sustainability and help us make more informed decisions to protect our planet.

AI Career Spotlight: James Leoni

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.

AI Career Spotlight: Maria Mestre

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!

AI Career Spotlight: Svetlana Stoyanchev

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 Svetlana Stoyanchev who’s a Senior Research Engineer & Team Lead at Toshiba Research. She’s spent over two decades in the field of human-computer dialogue, figuring out how we can speak to machines.

“Given the rapid pace of change in this field, it’s essential to keep an open mind and adapt to new developments.”

With a PhD from Stony Brook University and postdoc at Columbia, her work since has spanned industry and academia.

Tell us a bit about your job

As a researcher at an industrial lab, I engage in a variety of tasks, including writing research proposals, conducting experiments, programming prototype systems, and writing and reviewing research papers. I collaborate with university professors to supervise Masters and PhD students while also pursuing my own research agenda. My current project focuses on designing a natural language interface that enables users to communicate with a robot in a virtual environment. I aim to create an interactive system that communicates naturally, allowing for corrections, and clarifications, and continuously learns from the user. What I love most about working in a research laboratory is the opportunity to collaborate with a brilliant and supportive team, generate new ideas and continuously learn new things.

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

While pursuing my Master’s in Computer Science at New York University, long before the era of deep learning, I took AI courses where we explored logical problem solvers, applied decision trees to play chess, and used syntactic parsers for language translation. I was captivated by the ability to create systems that can reason and solve problems autonomously. Driven by an interest to apply AI to language, I joined a PhD program at Stony Brook University, where my journey working on human-computer dialogue began.

Tell us about a career choice you’ve made along the way

Throughout my career, I have navigated between academia and industry. Before starting my PhD, I worked as a software engineer, where I learned to build systems. This experience proved invaluable for my research on dialogue, which often requires system integration to evaluate research questions through interactions with real users. I have also held research positions at Columbia University and the Open University, which deepened my insight into academic research and prepared me for work in industrial research labs. While I find theoretical questions fascinating, I am equally interested in practical applications and exploring how AI can impact society.

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

In my current and past roles, I have honed my ability to identify and formulate research questions, which has been instrumental in developing my vision for impactful research. Effective communication with colleagues and collaborators at every stage of a research project has enhanced my communication skills, allowing me to discuss ideas and present research outcomes clearly. Supervising Masters and PhD students has provided me with valuable experience in delegation, communication, and collaborative problem-solving. Additionally, organizing workshops and serving as a program co-chair for conferences have been excellent opportunities for building my leadership skills.

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

AI is an exciting field, whether you’re working on cutting-edge research questions or applying solutions to real-world problems. Given the rapid pace of change in this field, it’s essential to keep an open mind and adapt to new developments. For those considering an academic career, I recommend choosing a research topic that will remain relevant as more powerful models emerge.

What are you excited for in the future of AI?

It’s incredible to witness advancements in language processing and computer vision research with the rise of self-supervised language and vision models. I am curious to see if scaling the current transformer architecture will be sufficient for the emergence of the new AI capabilities and what other methods might lead to the further advancements.