Machine Learning Engineer

Staff Machine Learning Engineer – Software Engineering

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

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 App Folio’s AI initiatives.

• 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 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.

• 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.

• ML infra at scale:
  Built and operated production ML infrastructure on AWS – ECS, Sage Maker, 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 Lang Chain / Lang Graph 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.

• Experience training small language models for production use.
• GPU performance tuning (vLLM, Tensor
  
  RT, 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.

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

At App Folio, 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.