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

• Comfortable across the stack: from CUDA kernels to Kubernetes manifests to Grafana dashboards

A big plus: experience with Rust, custom Triton kernels, benchmarks

Why White Circle

• Salary of $100,000 to $150,000 + equity

• 20 days of paid vacation

• Work from Paris (hybrid) + relocation package

• Best medical insurance in France

• All the hardware, tools, and services you need

• Covered subscriptions for AI agents and IDEs

• Team off-sites twice a year: we've recently been to the Alps and to Saint-Tropez

How we hire

1. Intro call with one of our colleagues

2. Complete the take-home exercise

3. Show your best during the technical interview

4. Final call with our CEO and CTO