ML Infrastructure Engineer

White Circle; Paris; Hybride; EUR 157 701 - 306 642; Plein temps

Avantages offerts par ce poste

Relocation package

Hybrid Paris/London work

Medical insurance

All necessary hardware/tools

Team off-sites

Résumé du poste

White Circle is hiring an ML Infrastructure Engineer to build the systems behind LLM post-training, RL, evaluation, and inference. You will collaborate with researchers near GPUs, training loops, data control systems, and cost-aware infra decisions.

You will work on inference stacks, agentic development environments, and end-to-end pipelines, in a hybrid Paris or London setting with relocation support.

Qualifications

• Experience designing distributed RL/post-training pipelines.
• Proficiency in PyTorch or JAX.
• Proficient Python with async/concurrency.
• Ability to debug distributed GPU workloads (CUDA, containers, NCCL).
• Experience with inference stacks (vLLM, TensorRT-LLM, Dynamo).
• Ability to map system metrics to model behavior and performance.

Responsabilités

• Build scalable RL and post-training pipelines for testing and ablations.
• Design data control systems for training data flow and policy updates.
• Tune training and inference end-to-end for throughput and reliability.
• Investigate how infra decisions affect learning dynamics and eval quality.
• Develop experiment infra: artifacts, evals, dashboards, reproducibility, cost visibility.
• Work on inference infra affecting post-training and evaluation loops.
• Create agentic development environments and tooling.

Connaissances

Distributed RL/post-training systems

PyTorch or JAX

Python (async, concurrency)

GPU/CUDA debugging

Inference stacks experience

System metrics to model behavior

Ownership mindset

Outils

PyTorch

JAX

CUDA

NCCL

vLLM

TensorRT-LLM

Dynamo

Description du poste

TLDR: We are looking for an ML Infrastructure Engineer to build the systems behind our LLM post‑training, RL, evaluation, inference, and agentic development workflows. You will work close to researchers, GPUs, training loops, data control systems, evals, inference stacks, and the infrastructure decisions that directly affect model learning and product quality.

About Us

White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural‑language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale.

We’ve raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and others.

We process over 100M+ API calls every month.

We fine‑tune and train our own LLMs so they run faster and cheaper than any open or proprietary model.

We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need.

You will

• Build robust, flexible, and scalable RL and post‑training pipelines, including smoke‑tuning runs for quality testing and approach ablations.
• Design data control systems that govern what the model sees, when it sees it, and how training data flows through rollouts, replay, filtering, evaluation, and policy updates.
• Tune training and inference end‑to‑end for high throughput across the systems that matter: networking, memory, compute scheduling, data loading, storage, checkpointing, and I/O.
• Investigate how infrastructure choices affect learning dynamics, eval quality, model behavior, and training stability – staying close to the state of the art in LLMs, RL, and post‑training.
• Build infrastructure for model iteration: experiment runs, artifacts, evals, dashboards, failure inspection, reproducibility, and cost visibility.
• Work on inference infrastructure where it affects post‑training and evaluation loops.
• Build and improve agentic development environments: coding‑agent harnesses, browser/tool integrations, terminal/runtime sandboxes, repo‑aware workflows, and multi‑agent orchestration.
• Work closely with the team: plan future steps, discuss trade‑offs, share context early, and stay in touch while building.

You’ll fit right in if you

• Have designed, built, or maintained distributed RL/post‑training systems at scale and are fluent in their moving parts: rollouts, replay buffers, reward signals, data filtering, policy updates, evaluation loops, and failure analysis.
• Are familiar with deep learning frameworks such as PyTorch or JAX.
• Are proficient in Python, including concurrency, asynchronous programming, multiprocessing, and performance optimization.
• Can debug distributed GPU workloads across CUDA runtime, container runtime, driver versions, NCCL or equivalent communication layers, networking, storage, scheduling, and checkpointing.
• Have experience with profiling tools across the stack, for example py‑spy, PyTorch profiler, Nsight, perf, tracing, metrics, logs, or custom instrumentation.
• Have experience with inference stacks such as vLLM, SGLang, TensorRT‑LLM, Dynamo, or custom serving infrastructure.
• Can reason from system metrics back to model behavior: when latency, queueing, sampling, data order, rollout throughput, or infrastructure failures affect learning.
• Have a strong ownership mindset: you can take an ambiguous infrastructure problem, make it concrete, ship a working system, and improve it from real feedback.

A big plus

• A public builder footprint: open‑source contributions to RL, distributed ML, LLM training, inference, eval, or agent infrastructure – repos, PRs, benchmarks, papers with code, technical posts – and a good technical X/Twitter presence with live building, debugging threads, and useful interaction with strong builders.
• Experience in a high‑bar AI infra, research, or model environment such as xAI/Grok, Qwen, ByteDance AI infra/research, Prime Intellect, or similar teams.
• Custom training framework support or ownership: distributed training, fine‑tuning pipelines, trainers, schedulers, checkpointing, data loaders, model/eval integration, or performance tooling.
• Serious use of Claude Code, Codex, Kimi Code, Pi Agent, Droid, or similar agentic coding systems as a development surface.
• Experience with GPU clusters on Kubernetes, Slurm, Ray, custom schedulers, or cloud GPU orchestration.
• NCCL, UCX, NVSHMEM, RDMA, InfiniBand, RoCE, or EFA.
• Rust, C++, CUDA, Go, or systems‑level performance work.

Why White Circle

• You will be able to propose and run your own experiments and research ideas on modern ML infrastructure with very little friction.
• You will work on current ML infra problems, close to research, product needs, and real model iteration – not maintaining legacy systems for the sake of keeping them alive.
• You will have an unusually high contribution level for the size of the team; your systems decisions can change how quickly we train, evaluate, ship, and improve models.
• You will have room to dig into areas of your own interest, as long as they help the company build better, faster, safer AI systems.
• Paid time off in line with your local regulations, no matter where you work from.
• Work from Paris (hybrid) with a relocation package available, or work from London (note: we are currently unable to provide relocation support and medical insurance for London‑based roles).
• Comprehensive medical insurance for our France‑based team.
• All the hardware, tools, and services you need.
• Covered subscriptions for AI agents and IDEs.
• Team off‑sites twice a year: we’ve recently been to the Alps and to Saint‑Tropez.

How We Hire

• Introductory call with HR (25 min)
• Take‑home test task
• Technical interview with Head of Applied Research (60 min)
• Final conversation with our CEO (45 min)

Please submit your application in English.

Compensation Range: $180K - $350K