TL;DR: 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 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 recently raised our Series A funding round, taking our total funding to $70M. Our investors include 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 CEO