Realtor.com Innovation, Technology & Agility

How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?

We’re using AI to augment our day-to-day workflows like everyone else, but one of my favorite applications is creating ad-hoc analyses of user feedback. In an earlier era of ML, we would spend weeks to months developing tools to summarize customer feedback, and even then, I could only produce a list of words that required thorough interpretation to make sense of. Now, product and engineering stakeholders are summarizing and exploring user feedback with custom prompts on the fly.

Can you share some examples of how AI/ML has directly contributed to enhancing your product line or accelerating time to market?

We help people find homes and connect with real estate agents who guide them through their home-buying, selling and renting journeys. At the heart of our platform is advanced search and recommendations technology, powered by AI/ML. We’ve evolved through multiple waves of ML sophistication — from basic statistical models, to deep learning, to foundation models. 

For years now we’ve enhanced our search experience with intelligent listing insights from both image and text. Now, we’re raising the bar by developing LLM-driven solutions designed with robust safety and ethics guardrails ensuring that our next stage provides meaningful, trustworthy value for our users.

What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?

I love the hackathon culture at Realtor.com. There’s nothing like a demo to flesh out an idea and inspire teams to tackle a project. We use hackathons as a way to encourage continuous learning and curiosity, and many of our AI/ML innovations have come from these demos. 

Throughout the year, we collect and share what we’re learning during well-attended weekly checkpoints. We also work with our cloud providers and generative AI platform hosts to gain early access for research on production-ready capabilities. Beyond what I learn from my colleagues, I regularly enhance my own awareness by reading newsletters and listening to podcasts I’ve collected over time.

What project are you most excited to work on in 2025? What is particularly compelling about this work for you?

I'm excited to deliver Realtor.com's first on-demand image tagging service for MLS partners in 2025. This groundbreaking initiative features our first GPU-powered real-time API, leveraging OpenAI's CLIP and Google's ViT models to generate highly accurate and descriptive tags for real estate images — a significant improvement over our current system. 

I was deeply involved in building this from scratch, driving every phase: requirements gathering, model evaluation, Kubernetes-based infrastructure design and API implementation. Tackling challenges like GPU optimization, library compatibility and low-latency inference was both demanding and rewarding. 

This project modernized our tech stack, reducing maintenance overhead and setting the stage for future ML initiatives. The new tagger supports more tags with superior accuracy and performance. It's a cornerstone in Realtor.com's strategy to productionalize ML models efficiently, setting a precedent for faster go-to-market timelines for real-time inference applications.

What does the roadmap for this project look like? 

The roadmap for this project has been structured into several phases: requirements gathering, proof-of-concept evaluations, architectural design, implementation and iterative testing. 

We're now in the final stage focused on optimizations and production deployments. This project has truly been a cross-functional effort, requiring close collaboration with the machine learning team to fine-tune the CLIP and ViT models, the Industry Tools team to ensure seamless integration with MLS partner systems and the CI/CD platform team (Skyway) to streamline deployment processes. 

Content services played a critical role in defining and validating business requirements. A big challenge was coordinating priorities and bandwidth across stakeholders. To address this, I organized regular syncs, maintained a dedicated Slack channel and created a Confluence space to ensure transparency and alignment. 

On the technical side, implementing a GPU-accelerated real-time API introduced complexities around resource allocation, latency optimization and dependency management. We tackled these through collaborative problem-solving sessions, pair programming and iterative testing.

What in your past projects, education or work history best prepares you to tackle this project? What do you hope to learn from this work to apply in the future? 

Over my four years at Realtor.com, I've worked on various projects within content engineering, but this initiative stands out as the most technically ambitious and cross-functional effort I've led. My background in software engineering and experience with cloud-native technologies provided a strong foundation for tackling the infrastructure and deployment challenges. My prior work on data-intensive applications helped me understand the nuances of optimizing performance and resource utilization. 

This project has been a tremendous learning experience in the realm of machine learning operations. I gained hands-on experience with model deployment, GPU optimization and real-time inference, which are critical skills for AI-driven applications. I developed a deeper understanding of the trade-offs involved in infrastructure decisions, such as balancing cost, performance and scalability. I'm excited to apply these learnings to future projects especially those involving real-time machine learning and AI. This project has expanded my technical expertise and honed my ability to manage complex, cross-team initiatives; skills that will benefit me in driving innovation at Realtor.com.