Staff Machine Learning Engineer

AppFolio is hiring a Staff Machine Learning Engineer to advance the ML platform that powers Realm-X. In this hybrid role in Denver, CO, you will help run an always-on, observable, and cost-disciplined machine learning infrastructure on AWS. You will also partner with teams across Voice & Agents and Research ML to turn prototypes into production systems with SLOs, on-call rotations, and strong operational visibility, while applying AI safety and authorization guardrails for autonomous actions.

What you’ll do

• Design and operate AppFolio’s ML infrastructure on AWS, including ECS, SageMaker, GPU fleets, model serving, autoscaling, and cost controls.
• Optimize unit cost across AI applications using approaches such as 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 fall-backs and abstractions that support production needs.
• Build the training and fine-tuning stack for Small Language Models, covering data pipelines, GPU orchestration, and evaluation.
• Work with Voice & Agents and Research ML engineers to harden prototypes into production systems, including delivery of SLOs, on-call, and observability.
• Operate AppFolio’s AI safety and authorization layer, including AWS guardrails, scoped tool permissions, and human-in-the-loop gates for autonomous agent actions.

What you bring

• 7+ years of experience developing and scaling web-based applications, ideally in a SaaS environment.
• Systems thinker who focuses on platforms and long-term leverage rather than one-off features.
• Production builder who has built and scaled ML infrastructure in production with meaningful business impact.
• Comfort working in high ambiguity, turning unclear infrastructure problems into actionable direction.
• Owner-operator mindset with urgency, strong engineering judgment, and outcomes focus.
• Reliability mindset for ML infrastructure, including SLOs, on-call, observability, and postmortems.
• Hands-on experience operating production ML infrastructure on AWS: ECS, SageMaker, GPUs, autoscaling, and cost controls.
• Production experience with model serving for both LLMs and custom models, including understanding quantization, batching, and routing.
• Direct production integration experience with Google (Vertex / Gemini), OpenAI, and Anthropic APIs.
• Experience training or fine-tuning language models end-to-end, including deep learning, evaluation, and inference.
• Cloud-native engineering skills in Python, Docker, dependency management, and CI/CD for AI workloads.
• Working knowledge of LangChain / LangGraph and modern RAG patterns over structured and unstructured data.
• Demonstrated experience reducing unit cost of AI workloads without regressing quality or latency.
• Hands-on operating experience with AI guardrails, scoped tool permissions, and authorization layers for production AI systems.

Technologies you’ll work with

• AWS, ECS, SageMaker, GPU fleets
• Python, Docker, CI/CD
• Google (Vertex / Gemini), OpenAI, Anthropic
• LangChain, LangGraph, RAG, Small Language Models

Compensation & benefits

Compensation range: $200,000 - $275,000 base pay (annual).

The actual compensation will be determined by skills, education, experience, and internal equity. Compensation is part of a broader Total Rewards package, which may include additional benefits and discretionary bonuses based on role and employment type. Regular full-time employees are eligible for benefits (see here).

Location and hiring process

This role is located in Denver, CO and is hybrid. Find out more about AppFolio locations by visiting their site. All late-stage candidates complete an in-person meeting with an AppFolian as part of the hiring process.

Nice to have

• Bachelor’s, Master’s, or Ph.D. in Computer Science or related technical discipline.
• 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.