Staff Machine Learning Engineer - Leasing
About The Position
AppFolio is hiring a Staff Machine Learning Engineer to own the machine learning strategy and execution for its Realm-X Leasing Performer, making it production-grade, observable, and continuously improving. The role works across applied ML, agent systems, and leasing expertise, partnering with Leasing Engineering, Voice & Agents, and Research ML to turn prototypes into dependable customer-facing systems. This is a product-facing and infrastructure-focused role. The engineer will shape how the Leasing Performer reasons, acts, and learns while ensuring reliability, cost efficiency, and safety at scale. The position defines the ML roadmap for Leasing products, leads architecture and model quality for an autonomous leasing agent, translates research into production capabilities, and establishes evaluation practices and ML engineering standards. It also owns production outcomes, including SLOs, observability, cost discipline, and on-call readiness.
Requirements
- 7+ years of experience developing and scaling web-based applications, preferably in a SaaS environment.
- Experience building and supporting production ML systems at scale.
- Experience building and scaling production ML infrastructure with meaningful business impact.
- Experience leading architectural discussions, defining system design, and guiding technical decision-making.
- Has trained or fine-tuned language models end-to-end and is comfortable with deep learning, evaluation, and inference.
- Hands-on experience with LangChain or LangGraph and modern RAG patterns over structured and unstructured data.
- Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems, especially in agentic contexts.
- Ability to think in terms of platforms and long-term leverage and understand how ML infrastructure decisions compound over time.
- Ability to work effectively amid ambiguity and turn unclear infrastructure problems into clear direction.
- Ownership mindset, urgency, and focus on outcomes.
- Collaborative, humble, low-ego approach across ML, product, and engineering.
- Reliability mindset that includes SLOs, on-call, observability, and postmortems.
- Values work-life balance as a foundation for sustained high performance.
Nice To Haves
- Bachelor’s, Master’s, or Ph.D. in Computer Science or a related technical discipline.
- Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows.
- GPU performance tuning experience with vLLM, TensorRT, Triton, or similar tools.
- 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.
Responsibilities
- Define and drive the machine learning roadmap across Leasing products, identify high-leverage opportunities, make model and architecture decisions, and align with Product and Engineering leadership on a technical vision focused on customer outcomes.
- Lead ML development and architecture for AppFolio's autonomous leasing agent, including model quality, evaluation frameworks, and continuous improvement.
- Partner with Voice & Agents and Research ML to evaluate fine-tuning approaches, retrieval strategies, and agentic patterns, deciding which capabilities are ready for production.
- Build evaluation and experimentation infrastructure, define quality measures for leasing-specific tasks, and own metrics tied to customer outcomes.
- Establish ML integration patterns, standards, and practices for Leasing Engineering, including prompt engineering, RAG, fine-tuning, and model selection.
- Ensure production ML systems meet SaaS reliability expectations through SLOs, observability, cost discipline, and an effective on-call posture.
- Lead architectural discussions, define system designs, and guide technical decision-making.
- Understand leasing workflows and use that domain knowledge to guide technical decisions.
Benefits
- Regular full-time employees are eligible for benefits; details are referenced on the company site.
- Total Rewards package, with potential eligibility for discretionary bonuses based on role and employment type.
- Opportunities for growth and compelling total rewards.
- Coaching and mentorship, with time and tools to develop skills.
- Hybrid work environment described as flexible, personal, and connected.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
