Staff Machine Learning Engineer

Salary: $200,000 - 250,000 per year Requirements:

• We look for a systems-minded engineer who sees platforms and durable leverage, not just isolated features.
• We need someone with proven production experience building and scaling ML infrastructure that has delivered measurable business value.
• We value the ability to thrive in ambiguity and turn unclear infrastructure challenges into concrete direction.
• We want an owner-operator mindset with urgency, accountability, and a strong focus on outcomes.
• We expect someone who moves quickly and delivers impact while applying sound engineering judgment.
• We are looking for a humble, collaborative teammate with a low-ego approach who lifts up those around them.
• We value a sustainable approach to work, with an understanding that work-life balance supports long-term high performance.
• We need someone who treats ML infrastructure like any mission-critical production system, including SLOs, on-call support, observability, and postmortems.
• We require hands-on experience building and operating production ML infrastructure on AWS, including ECS, SageMaker, GPUs, autoscaling, and cost controls.
• We need production experience with model serving for both LLMs and custom models, including quantization, batching, and routing.
• We require direct production integration experience with Google Vertex/Gemini, OpenAI, and Anthropic APIs.
• We expect end-to-end experience training or fine-tuning language models, with comfort in deep learning, evaluation, and inference.
• We need strong cloud-native engineering skills, including Python, Docker, dependency management, and CI/CD for AI workloads.
• We look for working knowledge of LangChain, LangGraph, and modern RAG approaches across structured and unstructured data.
• We require demonstrated success reducing the unit cost of AI workloads without harming quality or latency.
• We need 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.
• Nice to have: GPU performance tuning experience with tools such as vLLM, TensorRT, or Triton.
• Nice to have: prior Staff-level experience at a company with a significant AI infrastructure footprint.
• Nice to have: experience with ontology-driven systems or knowledge graphs supporting AI applications.
• Nice to have: contributions to open-source ML infrastructure or LLM tooling.

• We will have you design and run our ML infrastructure on AWS, including ECS, SageMaker, GPU fleets, model serving, autoscaling, and cost controls.
• You will optimize AI spend across our applications through provider routing, caching, batch versus real-time decisions, model sizing, and inference efficiency.
• You will maintain dependable multi-provider LLM access across Google, OpenAI, and Anthropic, with practical fallback strategies and clean abstractions.
• You will build our training and fine-tuning stack for Small Language Models, including data pipelines, GPU orchestration, and evaluation workflows.
• You will partner with our Voice & Agents and Research ML teams to turn prototypes into production-ready systems with SLOs, on-call coverage, and observability.
• You will operate our AI safety and authorization layer, including AWS guardrails, scoped tool access, and human-in-the-loop approvals for autonomous actions.

• AI
• AWS
• CI/CD
• Cloud
• Docker
• Fine-tuning
• Support
• LLM
• Model Serving
• Python
• RAG
• vLLM
• AI Agents
• Copilot
• Excel
• Machine Learning

More:

We are AppFolio, the technology leader shaping the future of the real estate industry with an AI-native platform that is transforming property management end to end. Our Realm-X platform powers intelligent capabilities such as copilots, AI agentic workflows, and autonomous agents, built on a shared domain foundation that helps AI understand and act across property management operations. We offer a competitive base salary of $200,000 to $250,000, along with a comprehensive total rewards package and benefits for regular full-time employees. We support hybrid work, provide opportunities to grow through coaching and mentorship, and foster a collaborative culture built around impact, learning, connection, and high performance. All late-stage candidates complete an in-person meeting as part of our hiring process, and we are an Equal Opportunity Employer committed to an inclusive workplace.

last updated 33 week of 2026