Sometimes, a new or different professional path can emerge from an unexpected source. For Rebekkah H., who leads the AI program management team at Meta, her career journey began with a serendipitous chance.
Before taking on a leadership role at the tech company, Rebekkah H. gained experience working on strategic research initiatives at Columbia University followed by the Rockefeller Foundation (RF). Her time at RF was particularly notable; Rebekkah H. was part of a team that provided grants to research global challenges that appealed to the foundation’s philanthropic mission. When she eventually landed at Meta an opportunity arose to gain hands-on experience in the AI field.
“When I started at the company, we were in the initial stages of building our research partnerships with academic institutions, ramping up publishing papers, and sharing our research externally,” she says. “I was specifically tasked to build out the research operations for the AI team.”
At that time, the 30-person team aimed to work closely with universities and hire PhD students as interns. The AI team wanted to share their discoveries and latest technological developments with an academic audience, using open collaboration to drive innovation across the company and the field of AI.
This welcomed responsibility allowed Rebekkah H. to acquire beneficial technical expertise and exercise her desire to conquer new challenges at work. Focusing on AI-related projects and initiatives gave Rebekkah H. insight into the field’s ever-changing landscape.
“Something that seems like a small change or advancement can unlock new methodologies and fundamentally change how we do things,” she explains. “For example, this happened recently with large language models and generative AI. As the field evolves, so does how we develop products and build the infrastructure to continue company-wide innovation, making our operations more efficient and sustainable.”
Today, Rebekkah H. is a witness to the wide, limitless world of AI. She shares her most rewarding career achievement thus far at Meta, how she’s prioritized continuous professional growth, and the eye-opening lessons she’s learned as a leader.
You’ve been at Meta for nearly 10 years. What’s been the most rewarding part of your career at Meta so far?
The most rewarding part of my career has been growing and managing a team. Working with and around smart people has always been a job benefit, but identifying those people, providing learning opportunities, accepting new challenges, and enabling them to work in new spaces is the most difficult—and most rewarding—part of my job.
What unique challenges do you face as a leader collaborating with cross-functional teams? What are your top tips for successful cross-functional collaboration, care, and innovation?
I don’t think my challenges are unique. Most of the cross-functional teams I work on face the same issues: trying to build things that have never been built before, with new technology at a fast pace and enormous scale, all within an emerging and shifting policy landscape. Cross-functional teams are made up of experts in their fields, and they have their own opinions about the best way to proceed. We encounter shared challenges like aligning our priorities and trusting each other to do what we do best.
When it concerns care, collaboration, and innovation, my top tip is to create space. In a fast-paced environment, it can be difficult to remember that we’re all people with lives. I’ve found that encouraging people to prioritize their health and overall well-being leads to their best ideas.
How do you continue to grow professionally and keep learning in a field like AI, which is evolving so quickly? Do you have any personal strategies for continuous development?
Most of my learning and professional growth comes from connecting with others in the field but separate from the company. I push myself to take speaking engagements, attend panel discussions, and join networking events to meet new people and learn from them. My strategy for professional development is to observe what’s happening at other companies and across the industry, read about it, and then find someone to speak with about it.
What are the most valuable lessons you’ve learned over your career, and how have they shaped your approach to team leadership and innovation?
- Be good at what you’re good at. Be confident in your skills and continue to refine those skills throughout your career.
- Project manage your career. Consider the experiences you want to feature on your resume and strategize your job searches to get that experience. This can include working in certain industries (tech or finance), roles (analyst or manager), or sectors (private or government). Don’t waste your time if the opportunity doesn’t align with your goals.
- Strive for excellence without compromising your integrity. Put effort into everything you deliver, and do what you can to bring excellence out in others. Maintaining your integrity is the key to getting people to trust your decisions and support your leadership. If you don’t trust yourself, no one else will.
What’s one piece of career advice you wish you had received earlier in your professional journey?
Most of your career will be a testament to opportunities found through your network. Build relationships with people from different backgrounds, career levels, and skill sets because they’ll remember you and advocate for you when you’re not in the room. Opportunities will come within and across companies—and industries—based on someone passing along your name to someone looking to hire the right candidate.
What’s your top advice for someone starting their AI or program management career?
Be open to trying new things; don’t rely too heavily on job descriptions. I developed a willingness to take on projects that I had no relevant experience or background in, which has advanced my career and provided opportunities I wouldn’t have received if I’d stuck to the strict definition of “program management” I had in my head in 2008.
Regarding getting started in AI, seek to understand top trends in the field and the consumer space. Then, apply that to your area of study or current work experience to find an entry point. You don’t need to know everything about AI to shift your career in that direction, but familiarity with products, concepts, and current industry challenges will get you started.
