Staff Machine Learning Engineer

Who We Are Looking For

We're hiring a Staff Machine Learning Engineer to help move forward the ML platform that every AI initiative at AppFolio depends on.

Qualifications

• 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.
• Must Have:
• 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.

Nice to Have

• 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.