--- title: 'ML Infrastructure Engineer at White Circle' canonical: 'https://feeny.ai/job/ml-infrastructure-engineer-white-circle-paris-cjvfwvec3k00' type: 'job' last_seen: '2026-09-19' --- # ML Infrastructure Engineer at White Circle - **Company:** White Circle - **Location:** Paris, France - **Compensation:** $180k–$350k - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-07-06 - **Last confirmed live:** 2026-09-19 - **Apply:** https://jobs.ashbyhq.com/whitecircle/9a66770a-f312-4cd7-8d83-60de8a01c61e ## Job description 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 us [White Circle](https://whitecircle.ai/) 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 We’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 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 the systems that matter: 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 – staying close to the state of the art in LLMs, RL, and post-training - Build infrastructure for model iteration: 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: coding-agent harnesses, browser/tool integrations, terminal/runtime sandboxes, repo-aware workflows, and multi-agent orchestration - Work closely with the team: plan future steps, discuss tradeoffs, share context early, and stay in touch while building You’ll fit right in if you - Have designed, built, or maintained distributed RL/post-training systems at scale and are fluent in their moving parts: rollouts, replay buffers, reward signals, data filtering, policy updates, evaluation loops, and failure analysis - Are familiar with deep learning frameworks such as PyTorch or JAX - Are proficient in Python, including concurrency, asynchronous programming, multiprocessing, and performance optimization - Can debug distributed GPU workloads across CUDA runtime, container runtime, driver versions, NCCL or equivalent communication layers, networking, storage, scheduling, and checkpointing - Have experience with profiling tools across the stack, for example py-spy, PyTorch profiler, Nsight, perf, tracing, metrics, logs, or custom instrumentation - Have experience with inference stacks such as vLLM, SGLang, TensorRT-LLM, Dynamo, or custom serving infrastructure - Can reason from system metrics back to model behavior: when latency, queueing, sampling, data order, rollout throughput, or infrastructure failures affect learning - Have a strong ownership mindset: you can take an ambiguous infrastructure problem, make it concrete, ship a working system, and improve it from real feedback A big plus - A public builder footprint: open-source contributions to RL, distributed ML, LLM training, inference, eval, or agent infrastructure – repos, PRs, benchmarks, papers with code, technical posts – and a good technical X/Twitter presence with live building, debugging threads, and useful interaction with strong builders - Experience in a high-bar AI infra, research, or model environment such as xAI/Grok, Qwen, ByteDance AI infra/research, Prime Intellect, or similar teams - Custom training framework support or ownership: distributed training, fine-tuning pipelines, trainers, schedulers, checkpointing, data loaders, model/eval integration, or performance tooling - Serious use of Claude Code, Codex, Kimi Code, Pi Agent, Droid, or similar agentic coding systems as a development surface - Experience with GPU clusters on Kubernetes, Slurm, Ray, custom schedulers, or cloud GPU orchestration - NCCL, UCX, NVSHMEM, RDMA, InfiniBand, RoCE, or EFA - Rust, C++, CUDA, Go, or systems-level performance work ## Why White Circle - Competitive compensation package, including equity - Flexible time off - Paid time off in line with your local regulations, no matter where you work from - Work from Paris (hybrid) + relocation package - Best medical insurance in France - Learning and development support for courses, conferences, and opportunities to grow your skills - All the hardware, tools, and services you need - Covered subscriptions for AI agents and IDEs - Team off-sites twice a year: we’ve recently been to the Alps and to Saint-Tropez Process - Introductory call with HR (25 min) - Take-home test assignment - Technical interview with Head of Applied Research (60 min) - Final conversation with our CEO (45 min) Please submit your application in English. ## About White Circle ## Company Overview - **One-liner**: White Circle is a control layer for AI in production, providing safety, security, evals, and performance optimization through a unified API. - **Entity Type**: Private (Seed stage) - **Headquarters**: Dover, Delaware, United States (operational hub in Paris, France) - **Founded**: 2025 - **Founders**: Denis Shilov (Founder & CEO) ## Core Business - **Primary industry**: AI Governance, AI Security, AI Observability - **Target customers**: B2B, Enterprise (companies deploying AI agents, chatbots, or LLM-powered applications) - **Mission or purpose**: To give organisations visibility into how their AI behaves, help them respond when things go wrong, and provide a unified system for improving reliability, safety and compliance. ## Products & Services - **White Circle Platform**: A single API that integrates three layers: - **Protect**: Custom low-latency guardrails for blocking unsafe inputs, preventing jailbreaks, detecting prompt injections, PII leakage, and sensitive data leaks. - **Observe**: Real-time analytics including topic classification, user behavior clustering, custom metrics, error rate analysis, sentiment analysis, and risk scoring. - **Improve**: Model routing, dynamic prompt engineering, context enrichment, and automated optimization based on labelled user feedback. - **CircleGuardBench**: A proprietary benchmark for evaluating AI moderation models across harm detection, jailbreak resistance, false positives, and latency. - **KillBench**: A benchmark of hidden LLM biases in critical decisions (life-and-death scenarios). ## Market Standing - **Valuation/Market Cap**: Not disclosed - **Key Metric**: $11M raised in seed funding (announced May 2026) - **Notable Investors/Partners**: Backed by prominent AI and technology leaders including Romain Huet (ex-Head of Product @DeepMind), Dirk Kingma (Anthropic), Guillaume Lample (Mistral), Thomas Wolf (Hugging Face), Olivier Pomel (Datadog), François Chollet (Keras), Mehdi Ghissassi (ex-DeepMind), Paige Bailey (DeepMind), and David Cramer (Sentry). - **Growth Signals**: - 21 employees (+283.3% YoY, +9.5% monthly growth) - Monthly website traffic: 32,090 visits (+682.7% yearly) - Active job postings: 6 (monthly job posting growth +20%) - SOC 2 Type II and HIPAA compliant - Operates in 7 countries (France, Russia, Netherlands, Serbia, Switzerland, Georgia, Spain) ## Competitive Advantages - **Proprietary self-adjusting models** optimized for low-latency and high performance - **Unified platform** that combines safety, security, evaluation, and performance optimization – no need for multiple point solutions - **Enterprise-grade compliance** (SOC 2 Type II & HIPAA) - **Strong network effects** from backing by leaders at OpenAI, Anthropic, DeepMind, Hugging Face, and Datadog - **Automated red-teaming** and continuous improvement through user feedback ## Strategic Focus - Accelerate product development and expand the team across the US, UK, and Europe - Grow global customer base across healthcare, finance, hiring, and security verticals - Continue building benchmarks (CircleGuardBench, KillBench) to shape AI safety standards - Maintain deep integration with the AI open-source ecosystem ## Why Work Here - **Culture**: “Smart, friendly, and fun” team with a strong engineering and research focus (43% technical staff) - **Location**: Based in Paris, France with a hybrid work model (office + remote) - **Benefits**: - Strong salary - 20 paid days off (plus ability to take more for recovery) - Free education (company covers learning costs) - Team offsites several times a year - **Open roles** (June 2026): QA Engineer, Product Engineer (Backend), DevOps Engineer, Product Designer, AI Engineer (Audio), Data Engineer - **Impact**: Build the future of AI safety at a well-funded, high-growth startup with world-class investors ## Sources 1. [whitecircle.ai](https://whitecircle.ai) 2. [whitecircle.ai/careers](https://whitecircle.ai/careers) 3. [LinkedIn – White Circle](https://www.linkedin.com/company/whitecircle) 4. [Tech.eu – White Circle lands $11M](https://tech.eu/2026/05/12/white-circle-lands-11m-to-help-companies-secure-ai-systems/) ## Other roles at White Circle - [Production Manager](https://feeny.ai/job/production-manager-white-circle-new-york-ns09sg8xf428) — New York, NY - [Content Lead](https://feeny.ai/job/content-lead-white-circle-new-york-a57fyake800m) — New York, NY - [Talent Lead](https://feeny.ai/job/talent-lead-white-circle-global-4r6c7dgw1p84) — Global - [Finance Lead](https://feeny.ai/job/finance-lead-white-circle-new-york-s3jtsbzkwt7f) — New York, NY - [Brand & Visual Designer](https://feeny.ai/job/brand-visual-designer-white-circle-new-york-3v7jkx8gskhf) — New York, NY - [Founding Account Executive](https://feeny.ai/job/founding-account-executive-white-circle-us-san-francisco-or-rns9xb4d5gj6) — US San Francisco OR, NY - [Recruiter (Research)](https://feeny.ai/job/recruiter-research-white-circle-new-york-1vekq2rdfrvz) — New York, NY - [Founding Events Lead](https://feeny.ai/job/founding-events-lead-white-circle-new-york-wczngn43ffxc) — New York, NY - [Partnerships Manager](https://feeny.ai/job/partnerships-manager-white-circle-new-york-c3ep31m67xse) — New York, NY - [Senior Data Labeler](https://feeny.ai/job/senior-data-labeler-white-circle-paris-vtp78vea9yp4) — Paris, France