Olukunle Owolabi

Why did you choose to study at The University of Edinburgh?

I studied petroleum engineering in Nigeria, but as I approached graduation, I was thinking more about where the energy sector was headed. The push to reduce carbon emissions was becoming harder to ignore. I had already worked on a paper about reducing emissions from combustion engines, and that research made me want to look beyond petroleum. Full-time opportunities in Nigeria’s petroleum industry were also difficult to find. Together, these experiences drew me toward renewable energy.

Edinburgh’s MSc in Sustainable Energy Systems gave me the chance to explore those interests through both research and practical engineering work. Receiving a Mastercard Foundation scholarship made it possible for me to attend.

It was my first time travelling outside Nigeria, and I was excited to learn alongside people who brought different perspectives to energy challenges. I expected the MSc to lead me further into the energy sector. I did not know then that the computational work I would discover at Edinburgh would eventually take me into data science and machine learning.

What path has your career taken since graduation?

After graduating from Edinburgh, I moved to the United States to pursue a full-time PhD in mechanical engineering at Tufts University. I used data science and machine learning to study power grid reliability, building on the curiosity about data and computation that had grown during my MSc.

Post-graduation, I joined Meta’s Infrastructure Quantitative Engineering organization, where I worked on demand forecasting and capacity planning for infrastructure supporting Facebook and Instagram. I briefly left Meta to deepen my expertise in machine learning, building recommendation and personalisation systems.

I have since returned to Meta, where I work on improving the usefulness and reliability of AI products. That includes how they personalize experiences, retrieve information, use memory, and how we evaluate whether those systems work as intended. Across my career, I have used data to understand complex systems and improve their performance and reliability.

What is your present occupation?

I am a Lead Machine Learning Engineer at Meta, working on AI products. My focus is making these systems more useful and reliable through personalisation, memory, retrieval, and evaluation.

My week is roughly split between setting technical direction and hands-on engineering. I work with engineers, data scientists, product managers, and researchers to decide which problems to tackle, how to measure progress, and which approaches are worth pursuing. I spend the rest of my time coding, building and testing solutions, and studying the results. I enjoy being able to shape a project’s direction while staying close to the engineering work. The role also gives me room to contribute to research and publish, while seeing ideas make a difference in a real product.

What experiences do you feel helped you get to your current position?

Studying at Edinburgh’s School of Engineering through a Mastercard Foundation scholarship opened up possibilities I had not imagined for myself. It was my first time studying outside Nigeria. The MSc gave me a strong technical foundation and pushed me to do more independent research. By graduation, I felt ready for a PhD, and Edinburgh’s reputation helped me take that next step.

My thesis placement was just as important. I worked with complex, inconsistent electricity demand data from communities in Nigeria, building models to help make decisions about microgrid design. I enjoyed getting close to a real problem and figuring out what the data could tell us. That work led naturally to my PhD research on power grid reliability. In industry, I have carried the same approach into computing infrastructure and AI systems at scale.

How has your degree knowledge developed during your career?

I use the way of thinking I developed at Edinburgh more than any single technique. In the MSc, we had to understand how different parts of an energy system fit together, work within real constraints, and test whether a proposed solution would hold up in practice. My thesis also taught me to question the quality of the data before trusting a model built on it. Those habits carried into my PhD research and remained useful in my work today. Whether I am looking at infrastructure demand or evaluating an AI agents, I need to understand the system, decide what to measure, and be honest about what the results do and do not show.

How did work experience help you?

During my thesis placement with Nayo Tropical Technology in Abuja, I used electricity demand data from underserved communities to plan hybrid microgrids. Working with real, imperfect data showed me that I enjoyed using computation to solve energy problems. That interest shaped my PhD research on power grid reliability and later drew me into computational data science and machine learning.

Can you tell us of your achievements so far?

I am proud to have graduated from Edinburgh with distinction as a Mastercard Foundation Scholar and gone on to complete a PhD at Tufts. Much of my work since then has centred on a question that took shape during my MSc: how can we make complex systems more reliable?

I have published research on the electric power grid, including outage forecasting, risk indicators, and detecting outages when training data is limited. In industry, I have applied similar thinking to infrastructure supporting Meta’s products and to machine learning systems used at scale. My research also includes evaluating the reliability of AI systems. I am proud that this work has led to publications across energy, data science, and AI, while remaining connected to problems with real consequences for people.

What advice would you offer to students?

I would advise students to get to know their field broadly and pay attention to the work they enjoy most. You do not have to decide now which part of your degree will define your career. The skills you develop can take you into other fields.