MLE/MLOps — Teletype

TLDR: We're looking for an MLE / MLOps to own our inference stack – from optimizing serving engines to building vector search pipelines – bridging Research and Product to ship models that are fast, cheap, and production-ready.

About us

White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn't do. We automatically test, enforce, and continuously improve these policies at scale.

• We've raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and others
• We process over one hundred million API calls every month
• We fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model

What you'll do

• Own inference infrastructure end-to-end: optimize latency, throughput, and cost across our model fleet.
• Build and scale model serving with TensorZero, vLLM/SGlang/TRT, and Kubernetes.
• Design and maintain vector search pipelines with Vector storages.
• Familiarity with support metrics (SLAs, FCR, deflection) and ability to define service health KPIs.
• Turn research into product: grab experimental models from the research team, figure out what's production-ready, and ship it - formatting, sampling parameters, deployment, the whole thing

Who you are

• 3+ years shipping high performance ML systems in production, not just training notebooks
• Deep hands-on experience with inference optimization - you've debugged latency spikes and know the difference between theoretical and real-world throughput