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.
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