DescriptionWho We Are Looking ForWe're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day.
This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale.
Your Impact- 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.
Qualifications- 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.
Must Have- 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.
Nice to Have- 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.
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