Staff Machine Learning Engineer
About The Position
AppFolio is hiring a Staff Machine Learning Engineer to advance the ML platform supporting its AI initiatives. The role covers training, fine-tuning, inference, retrieval-augmented generation (RAG), evaluation, reliability, and cost management. The engineer will operate AppFolio’s AI cloud and help shape application, model, and agent design while partnering with Voice & Agents and Research ML engineers to bring prototypes into production and scale Realm-X across AppFolio’s customer base. The position focuses on ML infrastructure, applied AI, and cost discipline. It includes designing and operating AWS-based ML infrastructure, building training and fine-tuning capabilities for Small Language Models, maintaining access to multiple LLM providers, and operating production AI safety and authorization systems. Production systems are expected to have service-level objectives, on-call rotations, observability, and reliability practices.
Requirements
- At least 7 years of experience developing and scaling web-based applications, preferably in a SaaS environment.
- Experience building and operating production ML infrastructure on AWS, including ECS, SageMaker, GPUs, autoscaling, and cost controls.
- Production experience serving LLMs and custom models, with understanding of quantization, batching, and routing.
- Direct production experience integrating with Google Vertex or Gemini, OpenAI, and Anthropic APIs.
- Experience training or fine-tuning language models end-to-end, with knowledge of deep learning, evaluation, and inference.
- Strong Python, Docker, dependency management, and CI/CD skills for AI workloads.
- Working knowledge of LangChain or LangGraph and modern RAG patterns over structured and unstructured data.
- Demonstrated experience reducing unit costs of AI workloads without regressing quality or latency.
- Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems.
- Systems-thinking approach focused on platforms and long-term leverage.
- Ability to build and scale production ML infrastructure with meaningful business impact.
- Ability to work effectively amid ambiguity and turn unclear infrastructure problems into clear direction.
- Ownership mindset, urgency, outcome focus, and sound engineering judgment.
- Collaborative, humble, low-ego approach and ability to elevate colleagues.
- Reliability mindset that includes SLOs, on-call, observability, and postmortems.
- Values work-life balance as a foundation for sustained high performance.
Nice To Haves
- Bachelor’s, Master’s, or Ph.D. in Computer Science or a related technical discipline.
- Experience training Small Language Models for production use.
- GPU performance tuning with vLLM, TensorRT, Triton, or similar tools.
- Prior Staff-level role at a company with a significant AI infrastructure footprint.
- Experience with ontology-driven systems or knowledge graphs supporting AI applications.
- Contributions to open-source ML infrastructure or LLM tooling.
Responsibilities
- Design and operate AppFolio’s ML infrastructure on AWS, including ECS, SageMaker, GPU fleets, model serving, autoscaling, and cost controls.
- Optimize costs across AI applications through provider routing, caching, batch versus real-time processing, model-size selection, and inference economics.
- Maintain reliable multi-provider LLM access across Google, OpenAI, and Anthropic, including fallbacks and abstractions.
- Build training and fine-tuning infrastructure for Small Language Models, including data pipelines, GPU orchestration, and evaluation.
- Partner with Voice & Agents and Research ML engineers to productionize prototypes with SLOs, on-call rotations, and observability.
- Operate AI safety and authorization systems, including guardrails on AWS, scoped tool permissions, and human-in-the-loop gates for autonomous agent actions.
- Help advance the platform layer that enables Realm-X to scale across AppFolio’s customer base.
Benefits
- Regular full-time employees are eligible for benefits; specific benefits are referenced through a link in the posting.
- The total rewards package may include discretionary bonuses based on role and/or employment type.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
