ML Research Engineer

Role Summary

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.

Responsibilities

You will

• 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

• Strong Python + SQL with an engineering mindset: you can build reliable pipelines, not just notebooks.
• 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.
• Comfortable at scale: distributed processing, large-scale storage-querying, and performance-cost tradeoffs.
• You 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

Benefits

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

Why White Circle

• Paid time off in line with your local regulations, no matter where you work from
• Work from Paris (hybrid) with a relocation package available, or work from London (note: we are currently unable to provide relocation support and medical insurance for London-based roles)
• Comprehensive medical insurance for our France-based team
• All the hardware, tools, and services you need
• Meaningful equity package
• Covered subscriptions for AI agents and IDEs
• Team off-sites twice a year: we've recently been to the Alps and to Saint-Tropez

How we hire

• Introductory call with HR (25 min)
• Take-home test task
• Technical interview with Head of Applied Research (60 min)
• Final conversation with our CEO (45 min)

Please submit your application in English.

About the Company

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.