--- title: 'ML Research Engineer at White Circle' canonical: 'https://feeny.ai/job/ml-research-engineer-white-circle-paris-teap6be4pgk9' type: 'job' last_seen: '2026-09-19' --- # ML Research Engineer at White Circle - **Company:** White Circle - **Location:** Paris, France - **Compensation:** $120k–$250k - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-07-02 - **Last confirmed live:** 2026-09-19 - **Apply:** https://jobs.ashbyhq.com/whitecircle/49c8b015-2d99-4173-b9a7-b4a0a0316eff ## Job description TLDR: We are looking for several ML Engineers to train, post-train, and evaluate the LLMs at the core of our platform. This is hands-on modern model training work: large-scale data pipelines, SFT/RLHF/DPO-style alignment, reward models, distributed multi-GPU training, and evaluation. ## 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. ## What you’ll do - Turn petabytes of unstructured text into a structured, explorable view (topics, clusters, segments, trends, anomalies): iterate from “unknown unknowns” to stable definitions we can track. - Build scalable representation pipelines: sampling strategies, preprocessing/normalization, embeddings at scale, indexing, and retrieval to make the corpus searchable and analyzable. - Use LLMs pragmatically: labeling/classification, weak supervision, data enrichment, summarization, and automated diagnostics of inbound volumes (with cost/quality controls). - Deliver insights that change decisions: translate findings into product and operational actions (what data we have, what’s missing, where quality breaks, what to prioritize next). - Ship self-serve analytics: datasets, data models, and lightweight tools/dashboards so the team can explore and answer questions without ad-hoc requests. - Partner closely with engineering/research: align pipelines with production constraints (latency/cost/privacy), and integrate outputs into workflows. You'll fit right in if you - Have strong Python + SQL with an engineering mindset: you can build reliable pipelines, not just notebooks. - Have solid applied NLP/ML experience on real-world text: embeddings, clustering, topic modeling, semantic search, classification; you understand failure modes and how to debug them. - Are comfortable at scale: distributed processing, large-scale storage-querying, and performance-cost tradeoffs. - Know how to evaluate fuzzy problems: offline/online metrics, human-in-the-loop labelling, inter-annotator agreement, drift monitoring, and reproducibility. - Have prior work with safety/moderation datasets, policy/rule systems, or high-volume logging/observability A big plus - A public builder footprint: open-source models, datasets, or training frameworks on HuggingFace/GitHub, benchmarks, papers (workshop or main conference), or technical posts with real usage - Experience training models at a frontier or near-frontier lab, or leading open-source model releases with documented adoption - Experience with RL methods for LLMs beyond standard RLHF: online RL, GRPO-style methods, or novel alignment approaches - Experience with moderation, safety, or classification models at scale - Multilingual model training experience ## Compensation & benefits - Competitive compensation, including equity - Flexible time off - Office in central London/Paris with flexible hybrid setup - Relocation support if you’re moving to Paris, available after your probationary period - Premium private health insurance - Mental health support, including coverage for therapy when you need it - Lunch and dinner covered when you work from the office - Learning and development support for courses, conferences, and opportunities to grow your skills - All the hardware, subscriptions, tools, and services you need - Team off-sites twice a year: we’ve recently been to the Alps, Saint-Tropez, and Marbella Process - Intro call with Talent Team - Test assignment - Technical interview with Head of Applied Research - Final conversation with our CEO ## 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