Staff Machine Learning Engineer

AppFolio, Inc; Santa Barbara (CA); Hybrid; USD 200,000 - 250,000; Full time

Job summary

AppFolio, Inc is seeking a Staff Machine Learning Engineer to advance their ML platform, focusing on AI-driven applications in the real estate sector. This role is pivotal for designing, optimizing, and maintaining ML infrastructure using AWS, including services like ECS and SageMaker.

The ideal candidate should have proven experience in production ML environments, particularly with Google, OpenAI, and Anthropic APIs, alongside a strong background in data pipelines and operational excellence.

Compensation ranges from $200,000 to $250,000, reflecting the importance of this strategic role.

Qualifications

• Experience building and operating production ML infrastructure on AWS.
• Direct experience with Google, OpenAI, and Anthropic APIs in production.
• Comfortable with deep learning, evaluation, and inference.

Responsibilities

• Design and operate ML infrastructure on AWS.
• Optimize cost across all AI applications.
• Maintain reliable, multi-provider LLM access.

Skills

Systems thinking

Production building

Ownership mindset

Collaboration

Cost optimization

Tools

AWS

Python

Docker

LangChain

Job description

Staff Machine Learning Engineer – Software Engineering

Locations: Santa Barbara, CA; San Diego, CA; Remote - San Francisco, CA; Remote - Denver, CO.

Overview

We’re building an AI‑native platform for the real estate industry and are looking for a Staff Machine Learning Engineer to advance the ML platform that underpins all of AppFolio’s AI initiatives.

Your Impact

• 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 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.

Qualifications

• Systems thinker: Think in terms of platforms and long‑term leverage, not just features.
• Production builder: Built and scaled ML infrastructure in production with meaningful business impact.
• Ambiguity: Operate effectively in high ambiguity, turning unclear infra problems into clear direction.
• Owner‑operator: Take ownership with a founder/owner‑operator mindset, act with urgency, and focus on outcomes.
• Pace: Strong desire to move fast and deliver impact while maintaining sound engineering judgment.
• Collaboration: Humble, collaborative, low‑ego, and elevate those around you.
• Sustainability: Value work‑life balance as a foundation for sustained high performance.
• Reliability mindset: Treat ML infra like any other production system – SLOs, on‑call, observability, postmortems.

Must Have

• ML infra at scale: 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: 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.

Compensation & Benefits

• Base pay range: $200,000 – $250,000. Additional benefits and bonuses may apply.
• Regular full‑time employees are eligible for benefits.

Statement of Equal Opportunity

At AppFolio, we value diversity in backgrounds and perspectives. We are a proud Equal Opportunity Employer and welcome applicants of all races, colors, religions, sexes, sexual orientations, gender identifications, national origins, ages, marital statuses, ancestries, physical or mental disabilities, or veteran status.