Staff ML Engineer: Autonomous Leasing AI Lead

AppFolio; Chicago (IL); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Hybrid work model

Career growth opportunities

Job summary

AppFolio is hiring a Staff Machine Learning Engineer to own the ML strategy for Realm-X Leasing Performer, turning prototypes into reliable, observable production systems. You will work across Leasing Engineering, Voice & Agents, and Research ML to shape how the agent reasons, acts, and learns, balancing performance with cost and safety at scale.

You will lead architectural decisions, drive model quality and evaluation, and build the infrastructure for experimentation and observability.

Qualifications

• Experience building production ML systems at scale.
• Led architectural discussions and defined system design.
• End-to-end training/fine-tuning of language models.
• Hands-on with LangChain / LangGraph and RAG patterns.
• Experience operating AI guardrails and production safety.

Responsibilities

• Own ML strategy and leadership for Leasing products.
• Architect autonomous AI agents and evaluation pipelines.
• Translate research into production-ready features.
• Build metrics, SLOs, and observability for ML systems.
• Set standards for ML engineering practices across teams.
• Ensure reliability and cost discipline in production.

Skills

Systems thinker

Production builder

Domain curiosity

Ambiguity tolerance

Owner-operator

Collaboration

Reliability mindset

Sustainability

Tools

LangChain

LangGraph

TensorRT

Triton

Job description

AppFolio is hiring a Staff Machine Learning Engineer to own the ML strategy for Realm-X Leasing Performer, turning prototypes into reliable, observable production systems. You will work across Leasing Engineering, Voice & Agents, and Research ML to shape how the agent reasons, acts, and learns, balancing performance with cost and safety at scale.

You will lead architectural decisions, drive model quality and evaluation, and build the infrastructure for experimentation and observability.