ML Infrastructure Engineer

United States Digital Space LLC; Paris (TX); Hybrid; USD 125,000 - 182,000; Full time

Benefits offered by this job

Competitive salary + equity

Hybrid work from Paris with relocation

Top-tier medical insurance in France

L&D budget and hardware/tools

Team off-sites twice a year

Job summary

White Circle is seeking an ML Infrastructure Engineer to join its AI safety team focused on policy enforcement and optimization for AI systems. The role requires hands-on experience with distributed RL/post-training systems, Python, PyTorch or JAX, and GPU debugging, with relocation to Paris for a hybrid setup.

You will build scalable pipelines, data control systems, and inference infrastructures, while developing agentic toolchains and multi-agent environments for robust model iteration and

Qualifications

• Hands-on experience designing and running distributed RL/post-training systems at scale.
• 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.

Responsibilities

• 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.

Skills

Python concurrency

PyTorch

JAX

Distributed systems

CUDA

Profiling tools

Kubernetes

C++

Tools

NCCL

Nsight

PyTorch profiler

TensorRT-LLM

Job description

We're looking for an ML Infrastructure Engineer 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.