Principal Machine Learning Engineer

AppFolio•Santa Barbara, CA

15h•Hybrid

About The Position

We’re seeking a Principal Machine Learning Engineer to help define and lead the next generation of AI systems within Realm-X. 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.

Requirements

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).
Deep expertise across Traditional Machine Learning, Deep Learning, Generative AI / LLMs (prompting, fine-tuning, RAG, agents, tool, and skills use)
Strong understanding of the full ML lifecycle: data extraction, model training, evaluation, and deployment.
ML models integration in production software.
Expertise in Python, PyTorch, Numpy, AWS, Docker, SQL, embeddings, and RAG.
Experience with LangChain, LangGraph, and LLM observability tools (LangSmith).
Experience designing and operating production-grade ML systems at scale
Proficiency with AI coding tools and workflows (e.g., Copilot, ChatGPT, code generation tools)

Nice To Haves

Experience with ontology-driven systems, knowledge graphs, or semantic layers
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.)

Responsibilities

Own the technical vision and architecture for AI systems across Realm-X in partnership with senior leadership.
Design and deploy advanced AI Agentic systems that combine reasoning, planning, and execution.
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.
Architect and build scalable, multi-modal, and real-time AI applications, ensuring high-quality deployment standards.
Set the technical foundation for training, fine-tuning, evaluating, and deploying open source LLM and SLM to production for optimizing performance, latency, and cost.