Staff Machine Learning Engineer - Leasing

AppFolio; Santa Barbara (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

AppFolio is seeking a Staff Machine Learning Engineer to lead the ML strategy for Leasing products in Santa Barbara, California. You will define the machine learning roadmap, ensuring that the systems meet production SaaS reliability standards, while leading architectural discussions and managing the ML model development lifecycle.

This role involves collaborating with various teams to enhance communication strategies and streamline leasing operations through AI. A strong background in ML development and architectural leadership is essential for success in this position.

Qualifications

• Has built and supported production ML systems at scale.
• Experience leading architectural discussions and guiding technical decision-making.
• Has trained or fine-tuned language models end-to-end.

Responsibilities

• Define and drive the machine learning roadmap across Leasing products.
• Be the ML lead for AppFolio's autonomous leasing agent.
• Build the evaluation and experimentation infrastructure for ML changes.

Skills

ML Development at scale

Architectural Leadership

Inference & Training

RAG & agents

AI safety & authorization

Experience building ML systems for conversational AI

Tools

LangChain

LangGraph

GPU performance tuning tools

Job description

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 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. You do not have to build all of it, but you own the outcomes.

Qualifications

• 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 LangChain / LangGraph 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 AppFolio, we value diversity and are an Equal Opportunity Employer.