Note: The job is a remote job and is open to candidates in USA. AppFolio is building an AI-native platform that transforms how the real estate industry operates through intelligent products and workflows. The Staff Machine Learning Engineer will design and operate the ML platform supporting training, fine-tuning, inference, RAG, evaluation, reliability, safety, and cost optimization across the company’s AI initiatives.
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
Skills
- * 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
Benefits
- Regular full-time employees are eligible for benefits.
- Discretionary bonuses may be available based on role and/or employment type.
- Opportunities for growth.
- Coaching and mentorship with best-in-class leaders.
- Time and tools to develop your skills.
- Hybrid work with a flexible, personal and connected environment.
Company Overview
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