Luxury Presence is building the AI growth platform for real estate. Backed by Bessemer Venture Partners and other top investors, we're a Series C company on track to hit $100M in annual recurring revenue in the next six months. More than 87,000 real estate professionals, including over 30% of the WSJ Real Trends top 100 agents in the United States, use us to run and grow their business.
About the Role
We’re seeking a Staff Software Engineer to strengthen our real estate MLS data platform squad. You will build robust data pipelines and backend services that power:
• High-quality MLS and property data across 400+ feeds
• Property discovery and search on agent websites
• Personalized listing recommendations and other data-driven features
• Conversational and operational AI agents that streamline internal workflows
• The evaluation and monitoring infrastructure that keeps these systems improving over time
This role sits at the intersection of backend engineering, data infrastructure, and AI-powered products.
Who is the Data Platform Squad?
We make sure clean, reliable MLS listing records and user click-stream data are always available to our products and customers. Our current team—a mix of data engineers and software engineers—owns the entire listing pipeline: ingestion, transformation, and normalization across 400+ MLS feeds and other sources.
We also extend the platform to capture user-activity data for user-facing features such as personalized listing recommendations, and we build AI agents that automate feed onboarding and listing-issue triage, reducing manual effort for internal teams and clients and shortening the path from data to business impact.
What You’ll Do
Technical leadership & architecture
• Own the end-to-end architecture for MLS and property data: streaming and batch pipelines, microservices, storage layers, and APIs
• Design and evolve event-driven, Kafka-based data flows that power listing ingestion, enrichment, recommendations, and AI use cases
• Drive technical design reviews, set engineering best practices, and make high-quality tradeoffs around reliability, performance, and cost
Backend, data & platform engineering
• Design, build, and operate backend services (Python or Java) that expose listing, property, and recommendation data via robust APIs and microservices
• Implement scalable data processing with Spark or Flink on EMR (or similar), orchestrated via Airflow and running on Kubernetes where applicable
• Champion observability (metrics, tracing, logging) and operational excellence (alerting, runbooks, SLOs, on-call participation) for data and backend services
Streaming & batch data pipelines
• Build and maintain high-volume, schema-evolving streaming and batch pipelines that ingest and normalize MLS and third-party data
• Ensure data quality, lineage, and governance are built into the platform from the start—supporting analytics, AI/ML, and customer-facing features
• Partner with analytics engineering and data science to make data discoverable and usable (e.g., semantic layers, documentation, self-service tooling)
AI agents & data products
• Collaborate with ML/AI engineers to design and scale AI agents that automate MLS feed onboarding, listing discrepancy triage, and other operational workflows
• Work with frameworks such as PydanticAI, LangChain, or similar to integrate LLM-based agents into our data and service architecture
• Help define and implement evaluation, logging, and feedback loops so these agents and data-driven products continuously improve
Cross-functional impact & mentorship
• Collaborate closely with Product, Engineering, and Operations to shape the roadmap for our data platform, MLS capabilities, and AI-powered experiences
• Translate ambiguous business and customer problems into clear technical strategies and phased delivery plans
• Mentor and unblock other engineers; elevate the overall level of technical decision-making on the team via pairing, reviews, and design guidance
What You’ll Bring
Experience & scope
• 10+ years of professional software engineering experience, including owning production systems end-to-end
• Significant experience working with data-intensive or distributed systems at scale (high volume, high availability)
• Prior experience in a senior or staff/lead role where you influenced architecture, standards, and technical direction
Core technical skills
• Strong programming skills in Python or Java, with experience building microservices and APIs (REST/GraphQL)
Hands-on experience with Apache Kafka or similar event/messaging platforms (Kinesis, Pub/Sub, etc.)
• Deep experience with:
◦ Spark or Flink for large-scale data processing, across streaming and batch pipelines (on EMR or similar big-data compute)
◦ Airflow (or equivalent orchestration tools)
◦ Kubernetes for running data/compute workloads
• Strong SQL and data modeling skills; solid understanding of ETL/ELT patterns, data warehousing c