ML Infrastructure Engineer

Npv·Parissenior

In short

→Ingeniero de infraestructura para ML a escala, enfocado en sistemas de RL y post-entrenamiento.
→Diseña pipelines distribuidos, evaluación, rollouts y entornos agenticos para modelos de IA.
→Destacado: trabajar en una startup de seguridad de IA con backing de líderes de OpenAI, Anthropic y DeepMind.

No se requiere inglés explícitamente, pero se menciona trabajo en equipo internacional.

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What they ask for

✓Experiencia práctica en sistemas distribuidos de RL/post-entrenamiento a escala.
✓Python avanzado con enfoque en concurrencia, async y optimización de rendimiento.
✓Conocimiento profundo de PyTorch o JAX y debugging en GPU distribuidas.
✓Capacidad para rastrear métricas de sistema y relacionarlas con comportamientos del modelo.
✓Reubicación a París (modelo híbrido) obligatoria.
✓Ingeniero con experiencia en infraestructura de alto rendimiento para IA (IA xAI, Qwen, ByteDance, etc.)

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PyTorchJAXvLLMSGLangTensorRT-LLMDynamoKubernetesSlurmRayNCCL

Who should you write to at Npv?

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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. Find Jobs in France on Arbeitnow

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