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Staff Machine Learning Engineer
AppFolio
Remote, USA
Full-time
Senior Level
Remote
USD 200000-250000 yearly
Discovered 1 weeks ago
Free
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Description
- 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 their 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.
- 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 with a founder/owner-operator mindset, act with urgency, and focus on outcomes.
- Pace: You have a strong desire to move fast and deliver impact, while maintaining sound engineering judgment.
- Collaboration: You are humble, collaborative, and low-ego, and you elevate those around you.
- Sustainability: You value work-life balance as a foundation for sustained high performance.
- Reliability mindset: You treat ML infra like any other production system — SLOs, on-call, observability, postmortems.
- ML infra at scale: Has 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: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference.
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