Staff Machine Learning Engineer - Leasing

AppFolio is more than a company. We’re a community of dreamers, big thinkers, problem solvers, active listeners, and multipliers. At every opportunity, we set the pace while delivering innovation built to carry real estate into the future. One in which every experience feels effortless, yet meaningful. Where customers are empowered to take on any opportunity. We show up as one team, connected by our values to be a force for good. Because together, we have the power to create extraordinary outcomes for our customers, our communities, and ourselves.

We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise — working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day.

This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns — and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale.

Your Impact

• Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products — identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes.

• Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent — shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time.

• Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities — fine-tuning approaches, retrieval strategies, agentic patterns — and make the call on what's ready to ship and what needs more hardening before it reaches customers.

• Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence — defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes.

• Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML — from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard.