ML Infrastructure Engineer

white circle • United Kingdom

Relocation

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AI Summary

Build and optimize RL and post-training pipelines for LLM safety systems. Design scalable data control and evaluation infrastructure. Work closely with researchers to improve model learning and product quality.

Key Highlights

Build RL and post-training pipelines for LLM safety workflows

Design data control systems governing training data flow and model behavior

Work closely with researchers on infrastructure affecting model learning

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Key Responsibilities

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 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

Build infrastructure for model iteration including 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 including coding-agent harnesses, browser/tool integrations, terminal/runtime sandboxes, repo-aware workflows, and multi-agent orchestration

Work closely with the team to plan future steps, discuss tradeoffs, share context early, and stay in touch while building

Technical Skills Required

Python PyTorch or JAX Distributed GPU systems Inference stacks (vLLM, SGLang, TensorRT-LLM)

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Benefits & Perks

Paid time off in line with local regulations

Hardware, tools, and services provided

Team off-sites twice a year

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Nice to Have

Public builder footprint with open-source contributions

Experience in high-bar AI infra teams (xAI/Grok, Qwen, ByteDance AI)

Custom training framework support

Serious use of agentic coding systems (Claude Code, Codex, Kimi Code)

Experience with GPU clusters on Kubernetes, Slurm, Ray, or custom schedulers

NCCL, UCX, NVSHMEM, RDMA, InfiniBand, RoCE

Rust, C++, CUDA, Go, or systems-level performance work

Job Description

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

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