Staff Machine Learning Engineer - Leasing

AppFolio is building an AI-native platform that transforms how the real estate industry manages property operations and leasing. The Staff Machine Learning Engineer will own the ML strategy and execution for the Realm-X Leasing Performer, including autonomous AI agents, model quality, evaluation, and production reliability. The role partners across Leasing Engineering, Voice & Agents, and Research ML to translate ML research into scalable customer-facing systems.

Responsibilities:

• 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

Requirements:

• • 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