Staff Machine Learning Engineer - Leasing

About the Role

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 role is close to both the product and infrastructure, shaping how the Leasing Performer reasons, acts, and learns while ensuring it's reliable, cost-efficient, and safe at scale.

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 model and architecture bets, and aligning with Product and Engineering leadership.
• Drive the Development & Architecture for Autonomous AI Agents: Lead the ML efforts for AppFolio's autonomous leasing agent, shaping its communication with prospective tenants and streamlining leasing operations. Own model quality, evaluation frameworks, and continuous improvement.
• Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities (fine-tuning, retrieval strategies, agentic patterns) and determine readiness for customer deployment.
• Drive Model Quality and Evaluation: Build evaluation and experimentation infrastructure to enable confident ML changes, defining leasing-specific success metrics tied to real customer outcomes.
• Set the ML Bar for Leasing Engineering: Establish patterns, standards, and practices for ML integration (prompt engineering, RAG, fine-tuning, model selection) across the Leasing Engineering team.
• Operate with Production Discipline: Ensure ML systems meet SaaS reliability standards, including SLOs, observability, cost discipline, and on-call readiness.

Requirements

• Systems thinker: Focus on platforms and long-term leverage, understanding how ML infrastructure decisions compound over time.
• Production builder: Experience building and scaling ML infrastructure in production with measurable business impact.
• Domain curiosity: Willingness to understand leasing workflows to inform better technical decisions.
• Ambiguity: Ability to operate in high ambiguity, turning unclear problems into clear direction.
• Owner-operator: Founder mindset with urgency and focus on outcomes.
• Collaboration: Humble, low-ego, and able to work fluidly across ML, product, and engineering teams.
• Reliability mindset: Treat ML infrastructure like production systems (SLOs, on-call, observability, postmortems).
• Sustainability: Value work-life balance as a foundation for high performance.

Qualifications

• Must Have:
  • ML Development at scale: Built and supported production ML systems at scale.
  • Architectural Leadership: Experience leading system design and technical decision-making.
  • Inference & Training: 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/unstructured data.
  • AI safety & authorization: Experience with AI guardrails, tool permissions, and authorization layers in production agentic systems.
• Nice to Have:
  • Experience with conversational AI, leasing, or CRM workflows.
  • GPU performance tuning (vLLM, TensorRT, Triton).
  • Experience with ontology-driven systems or knowledge graphs.
  • Familiarity with real estate, property management, or leasing workflows.
  • Contributions to open-source ML infrastructure or LLM tooling.

Pay

The expected base pay range for this role is $200,000 - $250,000. Actual compensation will vary based on skills, experience, and internal equity. Additional benefits and discretionary bonuses may apply for eligible roles.

Benefits

• Comprehensive Total Rewards package for full-time employees (see details).
• Culture of high performance with growth opportunities and recognition.
• Coaching, mentorship, and development tools to support career growth.
• Hybrid work environment fostering flexibility, connection, and innovation.