Principal Machine Learning Engineer
Who We Are Looking For
We're seeking a Principal Machine Learning Engineer to help define and lead the next generation of AI systems within Realm-X, and to drive AppFolio's long-term autonomous Real Estate Performance Management (RPM) platform — autonomous AI agents that can deliver property management performance.
Qualifications
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.
Must Have
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).
Nice to Have
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.).