Staff ML Infra Engineer: Scale, Cost & Safety

AppFolio Inc; Santa Barbara (CA); On-site; USD 200,000 - 275,000; Full time

Job summary

AppFolio Inc. is seeking a Staff Machine Learning Engineer to advance the ML platform that powers Realm‑X, focusing on training, fine-tuning, inference, and cost control.

You will keep the AI cloud always-on, observable, and economical, collaborating with Voice & Agents and Research ML teams to mature prototypes into production systems. You’ll own elements of ML infrastructure, model serving for LLMs and custom models, and guardrails for safe autonomous AI actions, reporting to the ML platform

Qualifications

• 7+ years of experience developing and scaling web-based applications, preferably SaaS.

Responsibilities

• Design and operate AppFolio’s ML infrastructure on AWS — ECS, SageMaker, GPU fleets, model serving, autoscaling, and cost controls.
• Optimize cost across all AI applications — provider routing, caching, batch vs. real-time, model size selection, and inference economics.
• Maintain reliable, multi-provider LLM access across Google, OpenAI, and Anthropic with sensible fallbacks and abstractions.
• Build training and fine-tuning stack for Small Language Models, including data pipelines, GPU orchestration, and evaluation.
• Partner with Voice & Agents and Research ML engineers to harden prototypes into production systems with SLOs, on-call rotations, and observability.

Skills

ML infra

Production builder

Ambiguity tolerance

Owner-operator

Pace

Collaboration

Sustainability

Reliability mindset

Education

Bachelor’s degree in CS/related

Tools

AWS ECS

SageMaker

GPUs

Model serving

LangChain

LangGraph

CI/CD

Python

Job description

AppFolio Inc. is seeking a Staff Machine Learning Engineer to advance the ML platform that powers Realm‑X, focusing on training, fine-tuning, inference, and cost control.

You will keep the AI cloud always-on, observable, and economical, collaborating with Voice & Agents and Research ML teams to mature prototypes into production systems. You’ll own elements of ML infrastructure, model serving for LLMs and custom models, and guardrails for safe autonomous AI actions, reporting to the ML platform