Originally, Maysam Moussalem dreamed of being an architect. “When I was 10, I looked up to see the Art Nouveau dome over the Galeries Lafayette in Paris, and I knew I wanted to make things like that,” she says. “Growing up between Austin, Paris, Beirut and Istanbul just fed my love of architecture.” But she found herself often talking to her father, a computer science (CS) professor, about what she wanted in a career. “I always loved art and science and I wanted to explore the intersections between fields. CS felt broader to me, and so I ended up there.”
While in grad school for CS, her advisor encouraged her to apply for a National Science Foundation Graduate Research Fellowship. “Given my lack of publications at the time, I wasn’t sure I should apply,” Maysam remembers. “But my advisor gave me some of the best advice I’ve ever received: ‘If you try, you may not get it. But if you don’t try, you definitely won’t get it.’” Maysam received the scholarship, which supported her throughout grad school. “I’ll always be grateful for that advice.”
Today, Maysam works in AI, in Google’s Machine Learning Education division and also as the co-author and editor-in-chief of the People + AI Research (PAIR) Guidebook. She’s hosting a session at Google I/O on “Building trusted AI products” as well, which you can view when it’s live at 9 am PT Thursday, May 20, as a part of Google Design’s I/O Agenda. We recently took some time to talk to Maysam about what landed her at Google, and her path toward responsible innovation.
How would you explain your job to someone who isn’t in tech?
I create different types of training, like workshops and labs for Googlers who work in machine learning and data science. I also help create guidebooks and courses that people who don’t work at Google use.
What’s something you didn’t realize would help you in your career one day?
I didn’t think that knowing seven languages would come in handy for my work here, but it did! When I was working on the externalization of the Machine Learning Crash Course, I was so happy to be able to review modules and glossary entries for the French translation!
How do you apply Google’s AI Principles in your work?
I’m applying the AI Principles whenever I’m helping teams learn best practices for building user-centered products with AI. It’s so gratifying when someone who’s taken one of my classes tells me they had a great experience going through the training, they enjoyed learning something new and they feel ready to apply it in their work. Just like when I was an engineer, anytime someone told me the tool I’d worked on helped them do their job better and addressed their needs, it drove home the fourth AI principle: Being accountable to people. It’s so important to put people first in our work.
This idea was really important when I was working on Google’s People + AI Research (PAIR) Guidebook. I love PAIR’s approach of putting humans at the center of product development. It’s really helpful when people in different roles come together and pool their skills to make better products.
How did you go from being an engineer to doing what you’re doing now?
At Google, it feels like I don’t have to choose between learning and working. There are tech talks every week, plus workshops and codelabs constantly. I’ve loved continuing to learn while working here.
Being raised by two professors also gave me a love of teaching. I wanted to share what I’d learned with others. My current role enables me to do this and use a wider range of my skills.
My background as an engineer gives me a strong understanding of how we build software at Google’s scale. This inspires me to think more about how to bring education into the engineering workflow, rather than forcing people to learn from a disconnected experience.
How can aspiring AI thinkers and future technologists prepare for a career in responsible innovation?
Pick up and exercise a variety of skills! I’m a technical educator, but I’m always happy to pick up new skills that aren’t traditionally specific to my job. For example, I was thinking of a new platform to deliver internal data science training, and I learned how to create a prototype using UX tools so that I could illustrate my ideas really clearly in my proposal. I write, code, teach, design and I’m always interested in learning new techniques from my colleagues in other roles.
And spend time with your audience, the people who will be using your product or the coursework you’re creating or whatever it is you’re working on. When I was an engineer, I’d always look for opportunities to sit with, observe, and talk with the people who were using my team’s products. And I learned so much from this process.