Description

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.

This is a company-impact role. You will own mission-critical AI capabilities, shape long-term technical strategy, and act as a technical visionary and advisor across teams and leadership.

You'll operate at the intersection of traditional machine learning, deep learning, and generative AI, building systems that go beyond AI assistance into execution, automation, and optimization.

This role is for someone who doesn't just build systems - but redefines how they should be built.

Your Impact

• 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.

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.).

Location

Find out more about our locations by visiting our site.

All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process.

Compensation & Benefits

The compensation that we reasonably expect to pay for this role is: $264,000 - $330,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity.

Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type.

Regular full-time employees are eligible for benefits - see here.

#LI-KB1

Learn more at appfolio.com/company/careers