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3 Ways Salesforce Takes AI Research to the Next Level

Shelby Heinecke
Apr 11 - 5 min read

Written by Shelby Heinecke and Scott Nyberg.

In our “Engineering Energizers” Q&A series, we examine the life experiences and career paths that have shaped Salesforce engineering leaders. Meet Shelby Heinecke, a research manager for the Salesforce AI team. Shelby leads her diverse team on a variety of projects, ranging from identity resolution to recommendation systems to conversational AI, supporting cutting-edge initiatives spanning academia and Salesforce products.

When did you know you were interested in pursuing a career in artificial intelligence?

While pursuing my math PhD, I had several summer internships, ranging from traditional software engineering to machine learning. These inspired me to pivot my PhD research from traditional math topics to computer science-inspired topics. That ultimately led me to machine learning and paved my path to Salesforce.

Machine learning and artificial intelligence (AI) are a good mix of my personal interests. Both incorporate interesting math and engineering problems that can have immediate real-world impact.

Shelby explains what drove her decision to join Salesforce.

What is your AI team’s role and mission here at Salesforce?

My team’s role and mission focuses on pioneering new AI approaches for personalization. That is a big part of what we do.

To provide some context, when you use apps and services, you expect a certain level of personalization. The experience is similar when you are on social media — you expect to see articles and media recommended to you according to your preferences. That is an example of personalization.

From a business standpoint, personalization improves user experiences and engagement. And of course, personalization is not limited to social media — any data-based services you interact with can be personalized, or tailored, to your individual needs.

What are some interesting AI projects your team is working on?

We currently focus on three key projects: Identity resolution, recommendation systems, and conversational AI.

With identity resolution, the first step in personalizing any service involves collecting and preparing data; this often means merging and aligning multiple diverse data sources. We build state-of-the-art AI solutions to intelligently execute that step.

A look at some of the ways in which individuals may appear multiple times across data sources.

Recommendation systems are in nearly every website you use today — it is the engine that follows your feeds to optimize recommendations for media, who to follow, products, ads, and articles. These recommendations are personalized according to your previous clicks, views, purchases, etc. My team develops state-of-the-art recommendation models for academic research and Salesforce products.

Lastly, we develop cutting-edge conversational AI approaches for conversational systems. You have seen these systems emerging everywhere — think customer service bots on websites, etc. By leveraging the power of personalization, companies can learn about their customers from their conversation history, thus making these conversational systems more effective.

How has your leadership style evolved since joining Salesforce?

I first joined Salesforce as a research scientist, which is tantamount to being an individual contributor. In that role, I grew as a technical leader on projects ranging from identity resolution to recommendation systems. It was an awesome experience.
Recently, I became a manager, and I am also growing as a people leader.

How would you describe the culture within your AI team?

As a research lab, the foundation of our culture is innovation. From developing leading edge models for Salesforce products to pushing the boundaries of what’s been achieved in academic research, we live and breath at the frontier of AI. In fact, we are often defining new frontiers.

Beyond being innovative, what also makes us effective as a lab is that we are collaborative and agile. We work closely with a range of Salesforce technical and non-technical teams — rapidly developing new solutions in face of any real-world constraints.

Shelby shares a recent engineering challenge that kept her up at night.

How important is diversity to you and how does it strengthen your AI team?

Diversity is a critical ingredient for innovation and problem solving. The coolest ideas come from new perspectives gained from our individual, unique experiences. Most importantly, for the complex problems that our team faces in AI, new perspectives and new ideas are necessary.

My team consists of researchers with diverse expertise, ranging from natural language processing (NLP) to recommendation systems to machine learning theory. Collectively, our research spans empirical to theoretical, and our experiences everything from academia to industry.

With our unique perspectives, we come together to solve challenging AI problems.

What is your favorite sport to play?

I love CrossFit. I discovered it around a year and half ago. I love that it is infinitely challenging and community-oriented. For most workouts, you are technically performing individually, but there is so much fun and community in pushing your limits with others beside you who strive to do the same. We push each other to be the best we can be.

It is interesting how some of CrossFit’s themes overlap with my Salesforce AI team. As individual researchers, we want to be the best we can be. So I think we try to push each other to push our boundaries — enabling everyone to maximize their fullest potential.

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