ML Infrastructure Engineer
Npv · Paris
What this role requires
3 requirements, read out of the advert rather than guessed from the job title:
Also mentioned, not required: CUDA, Kubernetes, Slurm, Ray, Rust, C++, Go, English. Worth having, but their absence is not what gets a CV filtered out.
See how often each of these is required across open data roles in Europe.
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Job description
We're looking for an ML Infrastructure Enginee r to join White Circle , an AI Safety company building the policy enforcement and optimization layer for AI systems. Backed by $11M from senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, and DeepMind, White Circle processes 100M+ API calls monthly and runs its own LLMs in production.
You will
Build scalable RL and post-training pipelines, including smoke tuning runs for quality testing and ablations.
Design data control systems for rollouts, replay, filtering, evaluation, and policy updates.
Tune training and inference end-to-end for throughput: networking, memory, scheduling, data loading, storage, checkpointing, I/O.
Build infrastructure for model iteration (experiment runs, artifacts, evals, dashboards, reproducibility, cost visibility) and inference infrastructure for post-training and eval loops.
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Build agentic development environments: coding-agent harnesses, tool integrations, runtime sandboxes, multi-agent orchestration.
Requirements
Hands-on experience designing and running distributed RL/post-training systems at scale (rollouts, replay buffers, reward signals, policy updates, eval loops).
Strong Python (concurrency, async, multiprocessing, performance optimization) and PyTorch or JAX.
Debugging distributed GPU workloads across CUDA, drivers, containers, NCCL, networking, storage, and checkpointing.
Profiling across the stack (py-spy, PyTorch profiler, Nsight, perf, tracing).
Inference stacks: vLLM, SGLang, TensorRT-LLM, Dynamo, or custom serving.
Ability to connect system metrics to model behavior and learning dynamics.
Relocation to Paris (hybrid) required.
Bonus
Public builder footprint: open-source contributions to RL, distributed ML, inference, eval, or agent infra; active technical presence on X.
Experience at high-bar AI infra/research teams (xAI, Qwen, ByteDance, Prime Intellect, or similar).
Ownership of custom training frameworks, trainers, schedulers, or data loaders.
GPU clusters on Kubernetes, Slurm, Ray; NCCL, RDMA, InfiniBand, RoCE, or EFA.
Rust, C++, CUDA, or Go; serious use of agentic coding tools (Claude Code, Codex, or similar).
We offer
Competitive salary + equity.
Hybrid work from Paris with relocation package.
Top-tier medical insurance in France and flexible time off.
L&D budget, all hardware and tools you need, plus covered AI agent and IDE subscriptions.
Team off-sites twice a year.
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