MLOps Engineer

TLDR: We're looking for an MLOps Engineer to sit at the boundary between Research and Production. You'll own the infrastructure that takes a trained model and makes it production-safe: rollout pipelines, quality and latency gates, canary deployments, and the dashboards that decide whether a release ships or rolls back.

About us

White Circle https://whitecircle.ai/ 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 100M+ API calls every month

• We fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model

We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need.

You will:

• Integrate new text and multimodal models into our serving paths and verify they behave correctly under production-like traffic.

• Build and maintain rollout pipelines for frequent model releases.

• Create smoke, quality, and performance gates for model promotion.

• Operate local and cluster GPU deployments on Kubernetes.

• Build dashboards for latency, throughput, queue depth, GPU usage, fallback rate, and quality drift.

• Run A/B and canary rollouts for model, prompt, routing, and serving config changes.

• Debug production issues across model config, tokenizer, serving API, router, queue, Kubernetes, GPU runtime, and CI jobs.

• Optimize serving cost and reliability across mixed GPU capacity.

Who you are

• Experience with an inference serving engine such as SGLang, vLLM, Dynamo, or TensorRT-LLM, and a working understanding of the request lifecycle through gateway, router, frontend, worker, queue, and model engine.

• Solid Kubernetes GPU experience: NVIDIA device plugin, GPU scheduling, resource requests/limits, node affinity, taints, tolerations, and node pools.

• Understanding of multi-node communication libraries and kernels, CUDA runtime, and container runtime compatibility, and the ability to debug across those layers.

• Ability to design and implement CI/CD for model serving: image and config versioning, smoke tests, quality regression tests against benchmarks, latency/throughput gates, canary rollout, and rollback.

• Strong observability instincts — you can define the dashboards and alerts that decide whether a model gets promoted or rolled back (p50/p95/p99 latency, TTFT, TPOT, queue depth, GPU utilization/memory, error/timeout/OOM rates, fallback rate, route distribution, canary vs. baseline, cost per successful request).

• Production debugging across the whole stack from Rust to k8s configs.

• Clear communication of engineering tradeoffs.

Nice-to-haves

• Rust backend experience.

• NCCL, UCX, NVSHMEM, RDMA, InfiniBand, RoCE, or EFA.

• ClickStack / Datadog.

• Terraform for GPU infrastructure.

• DCGM exporter, Prometheus, OpenTelemetry.

• Experience with a high model rollout cadence (2–3 releases per week).

Why White Circle

• Paid time off in line with your local regulations, no matter where you work from

• Work from Paris (hybrid) with a relocation package available, or work from London (note: we are unable to provide relocation support for London-based roles)

• Comprehensive medical insurance for our France-based team (please note that we are in the process of setting up our UK office and therefore cannot offer medical insurance for London-based roles yet)

• 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. Introductory call with HR (25 min)

2. Take-home test task

3. Technical interview with Head of Applied Research (60 min)

4. Final conversation with our CEO (45 min)