Note: The job is a remote job and is open to candidates in USA. AppFolio is a technology leader transforming the real estate industry through innovative AI solutions. They are looking for a Staff Machine Learning Engineer to lead the ML strategy and execution for their autonomous leasing platform, ensuring it is production-grade and continuously improving.
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 don't have to build all of it, but you own the outcomes
Skills
- 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
- 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
- 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
Benefits
- We enable a culture of high performance, where delivering results is recognized by opportunities for growth and compelling total rewards.
- We partner with you to realize your potential by investing in you from the start.
- We're cultivating a team of big thinkers through coaching and mentorship with our best-in-class leaders, and giving you the time and tools to develop your skills.
- We excel at hybrid work by fostering an environment that feels flexible, personal and connected, no matter where we are.
- We create space to fuel innovation and collaboration, and we come together to celebrate, connect, and succeed.
Company Overview
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