ML Infrastructure Engineer

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 Open

AI, Anthropic, Hugging

Face, Mistral, and Deep

Mind, 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: v

  LLM, SGLang, Tensor

  RT-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 (x

  AI, Qwen, Byte

  Dance, Prime Intellect, or similar).
• Ownership of custom training frameworks, trainers, schedulers, or data loaders.
• GPU clusters on Kubernetes, Slurm, Ray; NCCL, RDMA, Infini

  Band, Ro

  CE, 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.

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