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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - Leasing - **Company:** AppFolio, Inc. - **Location:** Richardson, TX, United States - **Experience:** Expert - **Salary:** $200,000.0 - $275,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Artificial Intelligence, Software as a Service, Machine Learning, Language Modeling, Open Source Technology, Performance Tuning, Software Safety, Unstructured Data, Web Applications, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Deep Learning, Agentic-AI, Information Technology, Machine Learning Operations, TensorRT, Artificial Intelligence Governance - **Published:** October 17, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=3094d9a59919c4f8 ## About the Role * You have 7+ years of experience developing and scaling web-based applications, preferably in a SaaS environment. * Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. * Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. * Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. * Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. * Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. * Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. * Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. * Sustainability: You value work-life balance as a foundation for sustained high performance., * ML Development at scale: Has built and supported production ML systems at scale. * Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. * Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. * Training capability: Has 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 and unstructured data. * AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts., * Bachelor's, Master's, or Ph.D. in Computer Science or related technical discipline. * Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. * GPU performance tuning (vLLM, TensorRT, Triton, or similar). * Experience with ontology-driven systems or knowledge graphs supporting AI applications. * Familiarity with real estate, property management, or leasing workflows. * Contributions to open-source ML infrastructure or LLM tooling. ## Description * 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 don't have to build all of it, but you own the outcomes.