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Machine Learning Engineer

Job in Santa Barbara, Santa Barbara County, California, 93190, USA
Listing for: AppFolio, Inc.
Full Time position
Listed on 2026-10-18
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 200000 - 275000 USD Yearly USD 200000.00 275000.00 YEAR
Job Description & How to Apply Below

Office Hub
- Santa Barbara;
Office Hub
- Dallas, TX;
Office Hub
- San Diego, CA;
Remote Hub
- Denver, CO

Employment Type

Full time

Location Type

Hybrid

Department

Software Engineering

Compensation

App Folio is more than a company. We’re a community of dreamers, big thinkers, problem solvers, active listeners, and multipliers. At every opportunity, we set the pace while delivering innovation built to carry real estate into the future. One in which every experience feels effortless, yet meaningful. Where customers are empowered to take on any opportunity. We show up as one team, connected by our values to be a force for good.

Because together, we have the power to create extraordinary outcomes for our customers, our communities, and ourselves.

Realm-X is App Folio's AI-native platform powering this transformation. It enables a new generation of intelligent capabilities across our products, including Realm-X Assistant (copilot), Flows (AI Agentic workflows) and Performers (autonomous AI Agents). Realm-X serves as both a foundation for internal teams to build and scale AI-powered products
, and a core layer delivering intelligent, high-impact experiences directly to our customers
.

At its core, Realm-X is built on a structured domain ontology and a set of shared business primitives
—such as transactions, actions, reports, metrics, and skills that enable AI systems to deeply understand and operate across the full context of property management workflows. This foundation allows us to build context-aware, action-oriented AI systems that go beyond simple assistance to power real automation and decision-making.

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 App Folio depends on — training, fine-tuning, inference, RAG, evaluation, and cost. You'll keep our AI cloud always-on, observable, and economical, while staying close enough to applications to influence model and agent design.

This role works at the intersection of ML infrastructure, applied AI, and cost discipline. You'll partner closely with our Voice & Agents and Research ML engineers to harden their prototypes into production systems, and help move forward the platform layer that lets Realm-X scale across App Folio's entire customer base.

Your Impact

ML Platform: Design and operate App Folio's ML infrastructure on AWS — ECS, Sage Maker, 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 their prototypes into production systems with SLOs, on-call rotations, and observability.

AI Safety & Guardrails: Operate App Folio's AI safety and authorization layer — guardrails on AWS, scoped tool permissions, and human-in-the-loop gates for autonomous agent actions.

Qualifications

You have 7+ years of experience developing and scaling web-based applications, preferably in a SaaS environment.

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…

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