Principal Machine Learning Engineer

AppFolio is building an AI-native platform that transforms how the real estate industry operates through intelligent products and workflows. The Principal Machine Learning Engineer will define the technical vision for Realm-X, build scalable AI agent systems, improve end-to-end machine learning infrastructure, and lead efforts toward autonomous property management through generative AI and reinforcement learning.

Responsibilities:

• Architect & Lead: Help define the technical vision and architecture for AI systems across Realm-X in partnership with senior leadership
• Scale Intelligent AI Agents: Design and deploy advanced AI Agentic systems that combine reasoning, planning, and execution, including multi-agent orchestration across specialist agents (e.g., maintenance, leasing, accounting, collections)
• Improve the Foundation: Establish platform primitives and abstractions to enable context-aware, action-oriented AI that goes beyond simple assistance to true automation. Improve the standards for end-to-end ML systems: data collection, model training, evaluation, deployment, and inference infrastructure
• Production Excellence: Architect and build scalable, multi-modal, and real-time AI applications, ensuring high-quality deployment standards
• ML for Autonomous Property Management: Drive AppFolio's transition toward autonomous property management operations. Use existing LLMs today and instrument the proprietary data collection now that will let us selectively train, fine-tune, and RL-optimize open source LLM and SLM for the RPM domain — optimizing performance, latency, and cost
• Reinforcement Learning for Agent Policies: Build the data and feedback loops needed to enable Reinforcement Learning over agent action policies in the partially observable, high-stakes property management environment

Requirements:

• Systems thinker: You think in terms of systems, platforms, and long-term leverage, not just features
• Production builder: You've built and scaled ML/AI systems in production with meaningful business impact
• Ambiguity: You operate effectively in high ambiguity, turning unclear problems into a clear direction
• Influence: You've led or influenced large, cross-team technical initiatives
• Originality: You introduce new ideas, architectures, or paradigms — not just implement existing ones
• Owner-operator: You bring a founder / owner-operator mindset: you take ownership, act with urgency, and focus on outcomes
• Pace: You have a strong desire to move fast and deliver impact, while maintaining sound engineering judgment
• Collaboration: You are humble, collaborative, and low-ego, and you elevate those around you
• Sustainability: You value work-life balance as a foundation for sustained high performance
• Vertical conviction: You bring genuine interest in winning a specific industry vertical (real estate) rather than chasing horizontal AI hype
• Master's or Ph.D. in Computer Science, Machine Learning, or a related field (required)
• 10+ years of experience building software systems, with significant focus on ML/AI (or equivalent impact)
• Combined academic and industry track record: Published research and shipped production systems
• Deep ML expertise: Traditional Machine Learning, Deep Learning, and Generative AI / LLMs (prompting, fine-tuning, RAG, agents, tool and skills use)
• LLM post-training: Direct, hands-on experience with LLM post-training — SFT, RLHF, DPO, and/or RL — at non-trivial scale
• Full ML lifecycle: Strong understanding of data extraction, model training, evaluation, deployment, and integration into production software
• Core stack: Expert in Python, PyTorch, NumPy, AWS, Docker, SQL, embeddings, and RAG
• Agent tooling: Experience with LangChain, LangGraph, and LLM observability tools (LangSmith)
• Production ML at scale: Experience designing and operating production-grade ML systems at scale
• Ontology & knowledge graphs: Applied experience with ontology-driven systems, knowledge graphs, or semantic layers used to model business domains for AI systems
• AI-native engineering: Proficiency with AI coding tools and workflows (e.g., Copilot, ChatGPT, code generation tools)
• Reinforcement Learning depth: Deep RL expertise applied to sequential decision-making under partial observability
• Experience designing evaluation and benchmarking systems for AI
• Background in distributed systems and real-time architectures
• Experience building platforms used by multiple engineering teams
• Contributions to industry thought leadership (publications, talks, open source, etc.)