Fionnuala, known as Finn by friends and colleagues, joined Robust Intelligence as a Machine Learning Engineer in January. She spent the previous four years at health tech company One Medical, initially as a Full Stack engineer and then moving into Machine Learning in the latter half of her time there. In our most recent employee fireside chat, Finn shared her experience becoming an engineer and, specifically, breaking into machine learning as a woman in a male-dominated field.

Can you share a personal or professional success story/accomplishment?

Breaking into the Machine Learning space as a woman.

When I started at Berkeley, I had no plan to study Computer Science. I loved math and was interested in neuroscience and philosophy, but hadn’t been exposed to engineering. Through these interests, I started studying Cognitive Science, and through this major had to take the intimidating ‘Intro to Computer Science’ class at Berkeley. While challenging, I loved the real world application of logic and problem solving. I also luckily had a couple of female friends in the class. I decided to pursue more Computer Science classes, and as I entered into more advanced classes, the number of women continually dropped. Time and time again I would find myself in an all-male project team. 

Starting work, I again felt imposter syndrome in the male dominated engineering field. Yet, I luckily had some amazing female mentors at One Medical. After two years working on full stack development, I took a step back to assess where I wanted to go in my career and what problems I wanted to solve.

Around this time, a Machine Learning team began at One Medical. As the team was being built, I took a keen interest in what they were working on and the problem space. During my free time, I began taking ML courses, working on hackathons, and doing side projects to build models using healthcare data. Turns out the ML space of engineering was even more male dominated. The team was made up of six at the time—all male—and so again I pushed my way into the boys club. I was allowed to do a rotation, and I worked hard to prove that I could be a capable machine learning engineer. I loved the work and continued to learn in my free time. There was no headcount on the team, but I excelled and nevertheless secured a spot. Breaking into a field where you are the minority takes determination – you must be bold and showcase your abilities. If I hadn’t pushed to get my foot in the door, I wouldn’t be at Robust Intelligence working on exciting, complex machine learning problems today. 

What did you do before joining Robust Intelligence?

Prior to Robust Intelligence, I worked at One Medical for over four years. I actually started as an intern on the Technology team while I was a student at UC Berkeley. For the first two years, I worked as a full-stack engineer building the core of the One Medical product – its Electronic Health Record, a database of all patient’s health data. The company was in an exciting growth stage, and went public in early 2020. Around this time, I joined the Machine Learning team, where I built NLP models and pipelines to pull insights out of masses of unstructured clinical data.

While building machine learning models within the healthcare landscape I grew concerned with how laissez-faire ethics are in ML. There is a lack of emphasis on data constraints, drift, and bias, and a strong emphasis on productionizing anything ML related. I started a data bias and fairness group to discuss solutions and analyze the models for potential bias, looking at it through a health equity lens. 

What's your favorite thing about working at Robust Intelligence?

My favorite thing about working at Robust Intelligence so far is definitely the team. Everyone is passionate about solving AI risk and digging into complex machine learning problems. It is fun to be in an environment where, because everything is changing constantly, new ideas are welcomed and decisions can be made quickly.

After working remotely due to the pandemic for the past two years, it has been really refreshing to be around coworkers in an office again. I’ve been very impressed by the team culture – everyone genuinely enjoys spending time with each other. The company is small enough where you can get to know everyone on a personal level, and the weekly team runs, Tahoe company ski trip, and Friday lunches ordering lasagna from Marcella’s really show that. 

It is a great feeling to walk into work both excited to solve the problems at hand and excited to spend time with your coworkers. 

When are you happiest?

Trail running! I love going for a long trail run on a Saturday morning, followed by getting coffee and pastries. I enjoy both running with friends and by myself as a meditation. There are amazing trails surrounding San Francisco. My favorite place to go is just 30 minutes from my house across the Golden Gate Bridge in the Marin Headlands. I also love planning trips around long day hikes and new trails – it’s a great way to explore a new area. 

I recently completed my first marathon, Big Sur International Marathon, which went from Big Sur up to Carmel on Highway 1, with some big hills along the way. It was an incredible experience, and I ended up finishing 11th in my age group. Earlier this year, I also ran a trail half marathon in Marin and won first place in women’s! Next on my list is running the Rim to Rim Grand Canyon trail and signing up for my first ultra.