
ML Infrastructure Engineer
Tentang pekerjaan
Rangkuman Get Karier, bukan salinan iklan aslinyaWhite Circle, perusahaan AI safety yang menjalankan LLM sendiri di produksi, mencari ML Infrastructure Engineer di Paris dengan pola kerja hybrid. Tugasnya membangun pipeline RL dan post-training yang bisa diskalakan, sistem kontrol data untuk rollout dan evaluasi, infrastruktur inference, serta lingkungan pengembangan agentic. Iklannya tidak menyebut angka gaji.
Kualifikasi
Yang perlu kamu siapkan, versi ringkas- Wajib: pengalaman merancang dan menjalankan sistem RL atau post-training terdistribusi dalam skala besar.
- Wajib: Python yang kuat (concurrency, async, multiprocessing) serta PyTorch atau JAX.
- Wajib: pengalaman debugging beban kerja GPU terdistribusi (CUDA, driver, container, NCCL) dan profiling lintas stack.
- Wajib: pengalaman inference stack seperti vLLM, SGLang, atau TensorRT-LLM.
- Wajib: bersedia pindah ke Paris.
- Nilai tambah: kontribusi open source, pengalaman di tim riset AI papan atas, GPU cluster di Kubernetes, Slurm, atau Ray, serta Rust, C++, CUDA, atau Go.
Deskripsi & syarat asli
Teks asli dari perusahaan, apa adanya. Tidak kami terjemahkan atau ubah.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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