Staff ML Engineer: Lead Autonomous Leasing Agent AI

AppFolio

Dallas (TX)

On-site

USD 120,000 - 160,000

Full time

14 days+
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Job summary

AppFolio is looking for a Staff Machine Learning Engineer to own the machine learning strategy for Leasing. You will define the roadmap for ML across leasing products and lead the development of an autonomous AI agent to enhance leasing operations.

The ideal candidate has experience with production ML systems, architectural discussions, and strong familiarity with deep learning techniques. This position aims to set standards for ML integration across teams, ensuring high-quality model performance and reliability.

Qualifications

  • Has built and supported production ML systems at scale.
  • Experience leading architectural discussions and defining system design.
  • Comfortable with deep learning, evaluation, and inference.
  • Hands-on experience with LangChain / LangGraph and RAG patterns.
  • Experience operating AI guardrails and permissions for production AI.

Responsibilities

  • Define and drive machine learning roadmap across Leasing products.
  • Lead ML development for AppFolio's autonomous leasing agent.
  • Partner with research ML to evaluate new capabilities.
  • Build evaluation and experimentation infrastructure for ML.
  • Establish patterns and practices for integrating ML in leasing.

Skills

ML Development at scale
Architectural Leadership
Inference & Training
RAG & agents
AI safety & authorization
Experience building ML systems for conversational AI

Job description

AppFolio is looking for a Staff Machine Learning Engineer to own the machine learning strategy for Leasing. You will define the roadmap for ML across leasing products and lead the development of an autonomous AI agent to enhance leasing operations.

The ideal candidate has experience with production ML systems, architectural discussions, and strong familiarity with deep learning techniques. This position aims to set standards for ML integration across teams, ensuring high-quality model performance and reliability.

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