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 usWhite 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 othersWe process over 100M+ API calls every monthWe fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary modelWe’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 ablationsDesign 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 updatesTune training and inference end-to-end for high throughput across the systems that matter: networking, memory, compute scheduling, data loading, storage, checkpointing, and I/OInvestigate 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-trainingBuild infrastructure for model iteration: experiment runs, artifacts, evals, dashboards, failure inspection, reproducibility, and cost visibility