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# Principal Machine Learning Engineer

- **Company:** AppFolio, Inc.
- **Location:** Santa Barbara, CA, United States
- **Experience:** Expert
- **Salary:** $264,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Amazon Web Services, Code Generation, Data Mining, Distributed Systems, Graph Database, Python (Programming Language), Machine Learning, NumPy, Open Source Technology, Microsoft Copilot, SQL Databases, Reinforcement Learning, Pytorch, LangChain, Retrieval-Augmented Generation, Large Language Models, Grafana, Deep Learning, Generative AI, AI Coding Agents, Data Layers, LangSmith, Information Technology, Machine Learning Operations, GPT, Docker
- **Published:** November 4, 2026
- **Apply:** https://www.juju.com/job/7_C02757C7D2F2D9D5F360709C11335355

## About the Role

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 HaveMaster'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 HaveReinforcement 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.).LocationFind out more about our locations by visiting our site.

## Description

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.QualificationsSystems 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