Staff Machine Learning Engineer - Leasing

About the role

We're innovators, changemakers, and collaborators building the AI-native platform where the real estate industry comes to do business. 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.

Who We Are Looking For

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.

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.
• 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.

Qualifications

• 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.

Must Have

• 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.

Nice to Have

• 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.

All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process.

Compensation & Benefits

The compensation that we reasonably expect to pay for this role is: $200,000 – $250,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate’s skills, education, experience, and internal equity. Regular full-time employees are eligible for benefits — see here.