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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - **Company:** AppFolio, Inc. - **Location:** Santa Barbara, CA, United States - **Salary:** $200,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Engineering, Continuous Integration, Python (Programming Language), Machine Learning, Language Modeling, Open Source Technology, Performance Tuning, Software Safety, Software Engineering, Systems Integration, Unstructured Data, Graphics Processing Unit (GPU), Autoscaling, Large Language Models, Deep Learning, AWS ECS, Low Latency, Machine Learning Operations, TensorRT, Docker - **Published:** June 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a3fb6a453c072bf0 ## About the Role Do you have experience in Software engineering?, * 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. Must Have * 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. * 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. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Celery on AWS ECS - the art of background tasks & continuous deployment](https://www.wearedevelopers.com/videos/561-celery-on-aws-ecs-the-art-of-background-tasks-continuous-deployment) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Tips, Techniques, and Common Pitfalls Debugging Kafka](https://www.wearedevelopers.com/videos/838-tips-techniques-and-common-pitfalls-debugging-kafka) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)