ML Research Engineer

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 1. Intro call with Talent Team 2. Test assignment 3. Technical interview with Head of Applied Research 4. Final conversation with our CEO