ML Infrastructure Engineer

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.

• 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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Source: Arbeitnow (https://www.arbeitnow.fr/jobs/companies/npv/ml-infrastructure-engineer-paris-383038)