Staff Machine Learning Engineer

AppFolio is building an AI-native platform that transforms how the real estate industry operates. The Staff Machine Learning Engineer will advance the ML platform supporting training, fine-tuning, inference, RAG, evaluation, and cost optimization, while operating reliable AI infrastructure and productionizing AI systems.

Responsibilities:

• ML Platform: Design and operate AppFolio's ML infrastructure on AWS — ECS, SageMaker, GPU fleets, model serving, autoscaling, and cost controls
• Drive AI Cost Discipline: Optimize cost across all AI applications — provider routing, caching, batch vs. real-time, model size selection, and inference economics
• Multi-Provider Reliability: Maintain reliable, multi-provider LLM access across Google, OpenAI, and Anthropic with sensible fallbacks and abstractions
• Training & Fine-Tuning Stack: Build the training and fine-tuning stack for Small Language Models, including data pipelines, GPU orchestration, and evaluation
• Productionize Research: Partner with Voice & Agents and Research ML engineers to harden their prototypes into production systems with SLOs, on-call rotations, and observability
• AI Safety & Guardrails: Operate AppFolio's AI safety and authorization layer — guardrails on AWS, scoped tool permissions, and human-in-the-loop gates for autonomous agent actions

Requirements:

• • Systems thinker: You think in terms of platforms and long-term leverage, not just features
• • Production builder: You've built and scaled ML infrastructure in production with meaningful business impact
• • Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction
• • Owner-operator: You take ownership with a founder/owner-operator mindset, 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
• • Reliability mindset: You treat ML infra like any other production system — SLOs, on-call, observability, postmortems
• • ML infra at scale: Has built and operated production ML infrastructure on AWS — ECS, SageMaker, GPUs, autoscaling, and cost controls
• • Inference platforms: Production experience with model serving for both LLMs and custom models; understands quantization, batching, and routing
• • Provider breadth: Direct experience integrating with Google (Vertex / Gemini), OpenAI, and Anthropic APIs in production
• • Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference
• • Cloud-native engineering: Strong Python, Docker, dependency management, and CI/CD for AI workloads
• • RAG & agents: Working knowledge of LangChain / LangGraph and modern RAG patterns over structured and unstructured data
• • Cost optimization: Demonstrated experience reducing unit cost of AI workloads without regressing quality or latency
• • AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems
• • Experience training Small Language Models for production use
• • GPU performance tuning (vLLM, TensorRT, Triton, or similar)
• • Prior Staff-level role at a company with a significant AI infra footprint
• • Experience with ontology-driven systems or knowledge graphs supporting AI applications
• • Contributions to open-source ML infrastructure or LLM tooling