Machine Learning Engineer - Leasing

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 App Folio'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 do not have to build all of it, but you own the outcomes.

• Must Have
  : ML Development at scale:
  Has built and supported production ML systems at scale.
• Must Have
  :
  Architectural Leadership:
  You have experience leading architectural discussions, defining system design, and guiding technical decision-making.
• Must Have
  :
  Inference & Training:
  Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference.
• Must Have
  : RAG & agents:
  Hands-on experience with Lang Chain / Lang Graph and modern RAG patterns over structured and unstructured data.
• Must Have
  : AI safety & authorization:
  Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems — especially in agentic contexts.
• Must Have
  :
  Other requirements captured in the original description as part of production ML leadership and reliability mindset have been preserved in description.
• Nice to Have
  :
  Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows.
• Nice to Have
  : GPU performance tuning (vLLM, TensorRT, Triton, or similar).
• Nice to Have
  :
  Experience with ontology-driven systems or knowledge graphs supporting AI applications.
• Nice to Have
  :
  Familiarity with real estate, property management, or leasing workflows.
• Nice to Have
  :
  Contributions to open-source ML infrastructure or LLM tooling.

Note: This description focuses on the responsibilities and qualifications for the Staff Machine Learning Engineer role within Realm-X Leasing Performer. Legal and equal opportunity statements are provided as part of the posting.

Equal Opportunity Statement: At App Folio, we value diversity and are an Equal Opportunity Employer.