whitecircle - MLOps Engineer

Paris or London$206k - $206k2mo ago

In Office EMEA Artificial Intelligence ML Engineer MLOps Kubernetes CUDA Rust Terraform Datadog Prometheus

Requirements

• 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. • 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).

Responsibilities

• 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.

Benefits

• 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 • 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)