ML Infrastructure Engineer @ npv labs
We're looking for an ML Infrastructure Engineer to join White Circle, a company building the policy enforcement and optimization layer for AI systems. The role involves designing and running distributed RL/post-training systems at scale, including smoke tuning runs for quality testing and ablations, data control systems for rollouts, replay buffers, reward signals, policy updates, eval loops, infrastructure for model iteration (experiment runs, artifacts, evaluations, dashboards, reproducibility, cost visibility), inference infrastructure for post-training and eval loops, agentic development environments: coding-agent harnesses, tool integrations, runtime sandboxes, multi-agent orchestration. The candidate should have hands-on experience designing and running distributed RL/post-training systems at scale, strong Python (concurrency, async, multiprocessing, performance optimization) and PyTorch or JAX skills, debugging distributed GPU workloads across CUDA, drivers, containers, NCCL, networking, storage, checkpointing, profiling across the stack (py-spy, PyTorch profiler, Nsight, perf, tracing), experience with inference stacks: vLLM, SGLang, TensorRT-LLM, Dynamo, or custom serving, ability to connect system metrics to model behavior and learning dynamics, public builder footprint contributions to RL, distributed ML, inference, eval, or agent infra, ownership of custom training frameworks, trainers, schedulers, or data loaders, GPU clusters on Kubernetes, Slurm, Ray; NCCL, RDMA, InfiniBand, RoCE, or EFA, serious use of agentic coding tools (Claude Code, Codex, or similar), competitive salary + equity, and a relocation package to Paris. The candidate should also be able to work from home in Paris with a relocation package.
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