Note: The job is a remote job and is open to candidates in USA. 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
Skills
- 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.)
Benefits
- Discretionary bonuses you may be eligible for based on your role and/or employment type.
- Opportunities for growth.
- Coaching and mentorship with best-in-class leaders.
- Time and tools to develop your skills.
- Hybrid work with a flexible, personal, and connected environment.
Company Overview
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