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We're innovators, changemakers, and collaborators. We're more than just a software company — we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio.

Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle — lead management, tour scheduling, follow-up, application processing, etc. — on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition.

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.

• 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.

• Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands — SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes.

• Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time.

• Production builder: You've built and scaled ML infrastructure in production with meaningful business impact — and you treat it like any other production system.

• Domain curiosity: You take time to understand the business workflows your systems serve — in this case, leasing — and use that understanding to make better technical bets.

• Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction.

• Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes.

• Collaboration: You are humble, collaborative, and low-ego — you elevate those around you and work fluidly across ML, product, and engineering.

• Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems.

• Sustainability: You value work-life balance as a foundation for sustained high performance.

• ML Development at scale: Has built and supported production ML systems at scale.

• Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making.

• Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference.

• Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference.

• RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data.

• AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems — especially in agentic contexts.

• Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows.

• GPU performance tuning (vLLM, TensorRT, Triton, or similar).

• Experience with ontology-driven systems or knowledge graphs supporting AI applications.

• Familiarity with real estate, property management, or leasing workflows.

• Contributions to open-source ML infrastructure or LLM tooling.