ML Infrastructure Engineer

Npv; Paris; Hybride; EUR 120 000 - 190 000; Plein temps

Avantages offerts par ce poste

Competitive salary + equity

Hybrid work from Paris with relocation

Top-tier medical insurance in France

L&D budget and hardware/tools coverage

Team off-sites twice a year

Résumé du poste

White Circle, an AI safety company, is hiring a ML Infrastructure Engineer to design and operate scalable RL and post‑training pipelines. You will build data control systems for rollouts, replay, evaluation, and policy updates, and tune training and inference end‑to‑end for throughput across GPUs, networking, and storage.

Expect work on experiment tooling, dashboards, and cost visibility. You will relocate to Paris on a hybrid basis and contribute to agent infrastructure, runtime sandboxes, and

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 and eval loops.
• Build agentic development environments: coding-agent harnesses, tool integrations, runtime sandboxes, multi-agent orchestration.

Description du poste

ML Infrastructure Enginee r

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).

Benefits

• 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.