Build and operate the ML platform that powers AppFolio’s AI-native Real Estate platform, ensuring scalable training, inference, and cost‑efficient operations across AWS and multi‑provider LLMs.

Company: AppFolio

Role: Staff Machine Learning Engineer

Location: Remote (San Francisco, CA; Denver, CO; Santa Barbara, CA; San Diego, CA)

Experience:

Experience building and operating production ML infrastructure at scale on AWS (ECS, SageMaker, GPUs, autoscaling, cost controls)
Production experience with model serving for LLMs and custom models, understanding quantization, batching, and routing
Direct experience integrating with Google Vertex/Gemini, OpenAI, and Anthropic APIs in production
Demonstrated cost optimization for AI workloads without regressing quality or latency
Hands‑on experience operating AI guardrails, scoped tool permissions, and authorization layers

Key Skills:

Python
Docker
dependency management
CI/CD for AI workloads
RAG and agents (LangChain, LangGraph, modern RAG patterns)
AWS (ECS, SageMaker, GPUs, autoscaling, cost controls)
Google Vertex/Gemini
OpenAI
Anthropic APIs

Qualification:

Strong Python, Docker, dependency management, and CI/CD for AI workloads
Experience with RAG and agents (LangChain, LangGraph, modern RAG patterns)
Systems thinker, production builder, owner‑operator, strong desire to move fast, collaborative, and reliable mindset

Role Focus:

Design and operate AppFolio's ML infrastructure on AWS, including ECS, SageMaker, GPU fleets, model serving, autoscaling, and cost controls
Optimize AI cost across all applications through routing, caching, batch vs. real‑time processing, model size selection, and inference economics
Maintain reliable multi‑provider LLM access across Google, OpenAI, and Anthropic, with fallbacks and abstractions
Build the training and fine‑tuning stack for small language models, including data pipelines, GPU orchestration, and evaluation
Productionize research prototypes with SLOs, on‑call rotations, and observability
Operate AppFolio's AI safety and authorization layer, including guardrails on AWS, scoped tool permissions, and human‑in‑the‑loop gates

Nice to have:

Experience training small language models for production use
GPU performance tuning (vLLM, TensorRT, Triton, or similar)
Prior staff‑level role in 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

Other:

Base salary $200,000 – $250,000 per year. Total rewards include benefits and potential discretionary bonuses
Equal Opportunity Employer, welcoming individuals of any race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, ancestry, physical or mental disability, or veteran status

Staff Machine Learning Engineer

Skills Required

Description