Building community intelligence: Hari Krishn Gupta’s ML innovation reshapes social platform interaction

Hari Krishna Gupta, a software engineering leader, who played a pivotal role in transforming the way content is organized on Nextdoor, one of the world’s leading neighborhood social networking platforms. As the driving force behind Nextdoor’s ‘Topics’ feature and its real-time machine learning (ML) prediction service, Hari offers insights into his innovative approach to building scalable, intelligent systems and how his work continues to shape the future of content categorization.

Can you tell us about your role in the development of Nextdoor’s ‘Topics’ feature?

Absolutely. My primary role at Nextdoor was as a full-stack engineer, and I spearheaded the development of Nextdoor’s first real-time machine learning prediction service. This feature allowed us to categorize user-generated content more effectively, giving the platform’s users a more organized and relevant experience. The idea behind the ‘Topics’ feature was to introduce precision in categorizing content using machine learning, so when users post something, our system can automatically analyze it and recommend the right category, making content discovery much easier.

What made this machine learning system unique and groundbreaking for Nextdoor?

The real breakthrough was creating a scalable, real-time system that could handle an ever-growing volume of user posts while maintaining accuracy. Our machine learning model wasn’t just about categorizing posts. We had to build an infrastructure that could process posts in real time and adapt to the changing nature of content. This was a significant challenge because, unlike traditional static systems, we had to constantly evolve the model to ensure it was making accurate recommendations at scale.

How did you manage to balance the technical complexity of machine learning with the practical needs of a social platform like Nextdoor?

The key was always keeping the end user in mind. It’s one thing to build a complex algorithm; it’s another to ensure that it improves user engagement and experience. Our team was focused on automating categorization so that users could discover relevant content without having to sift through endless posts. At the same time, we wanted to ensure that the system remained intuitive. The challenge was to implement a powerful yet simple solution that could be understood by both users and engineers, and that would fit seamlessly into the social experience on Nextdoor.

Can you explain how this machine learning model evolved over time and what it has now become?

Initially, it was a solution specifically designed for categorizing posts. But as we worked on it, we realized that the system was adaptable and could be leveraged for other ML-powered features across Nextdoor. What started as a basic categorization tool evolved into a much broader framework for intelligent content organization, improving features like recommendations and discussions. The flexibility and scalability of this system are what make it so impactful. It’s become the foundation for a lot of the platform’s future innovations.

What was the biggest challenge you faced during the development of this feature?

One of the biggest challenges was ensuring the system’s scalability while maintaining real-time processing speeds. With millions of users generating content every day, our system needed to handle diverse content types. We also had to optimize the system to be robust enough to handle unexpected patterns in user behavior and posts. The real challenge was making sure the model could adapt and still provide meaningful results, even as content trends and user behavior shifted.

How did your experience at Nextdoor shape your approach to machine learning and social networking platforms?

My time at Nextdoor reinforced the importance of user-centered design in machine learning. It wasn’t just about building fancy algorithms; it was about ensuring they brought real-world value to users. We had to be thoughtful about how we implemented the technology to ensure it aligned with the platform's mission of fostering community and connection. Working in such a fast-evolving space helped me develop a mindset of continuous improvement—constantly optimizing and refining based on real-time feedback.

Can you share what’s next for you in your career?

I’m really excited about the future. My focus is on building large-scale, impactful systems that use data and machine learning to improve user experience. I want to continue exploring the intersection of technology and human interaction. As technology evolves, I see so much potential for further innovations in social platforms, and I’m excited to be part of that journey.

Lastly, how do you view the future of machine learning in social networking?

I think machine learning is going to play an increasingly critical role in shaping social networks. The future of social platforms will be driven by intelligent systems that can understand and adapt to users’ preferences, behaviors, and needs. The key challenge will be ensuring that these systems remain user-friendly and transparent, allowing for personalized experiences without compromising privacy. I’m excited to see how machine learning continues to evolve in this space and how it can create more meaningful, engaging experiences for users.