ML Infrastructure Engineer

Npv; Paris; Hybride; EUR 90 000 - 140 000; Plein temps

Avantages offerts par ce poste

Equity

Hybrid work in Paris

Top-tier health insurance in France

L&D budget & hardware/tools

Team off-sites

Résumé du poste

White Circle, an AI Safety company, is hiring a ML Infrastructure Engineer to design and run distributed RL and post-training systems in production. The role focuses on scalable pipelines, data control for rollouts and policy updates, and end-to-end tuning for throughput and reliability.

You will build infrastructure for model iteration, evaluation, dashboards, and agent-centric development environments, with a hybrid Paris-based relocation.

Qualifications

• Experience designing and running distributed RL/post-training systems at scale.
• Strong Python and PyTorch or JAX.
• Debugging distributed GPU workloads across CUDA, drivers, containers, NCCL, networking, storage, and checkpointing.
• Profiling across the stack with py-spy, PyTorch profiler, Nsight, perf, tracing.
• Relocation to Paris (hybrid) required.

Responsabilités

• 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.
• Build infrastructure for model iteration (experiment runs, artifacts, evals, dashboards, reproducibility, cost visibility) and inference infrastructure for post-training.
• Build agentic development environments: coding-agent harnesses, tool integrations, runtime sandboxes, multi-agent orchestration.

Connaissances

Python

PyTorch/JAX

Distributed RL

GPU debugging

Profiling tools

Performance optimization

NVIDIA CUDA

Outils

CUDA

NCCL

Slurm

Ray

Kubernetes

Nsight

Description du poste

We're looking for an ML Infrastructure Engineer 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.