ML Research Engineer
Npv · Paris
What this role requires
4 requirements, read out of the advert rather than guessed from the job title:
Also mentioned, not required: embeddings, clustering, topic modeling, semantic search, classification, distributed processing, large-scale storage, querying, performance-cost tradeoffs. Worth having, but their absence is not what gets a CV filtered out.
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Job description
We're looking for ML Engineers to join White Circle , an AI Safety company building the safety, reliability, and optimization layer for AI systems through natural-language policies it automatically tests, enforces, and improves at scale. Backed by $70M (Series A) from top funds and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, and others, White Circle processes 100M+ API calls monthly and fine-tunes and trains its own LLMs to run faster and cheaper than open or proprietary models.
You will
Turn petabytes of unstructured text into a structured, explorable view: topics, clusters, segments, trends, anomalies.
Build scalable representation pipelines: sampling, preprocessing, embeddings at scale, indexing, and retrieval.
Use LLMs for labeling, weak supervision, data enrichment, and automated diagnostics, with cost/quality controls.
Translate findings into product and operational decisions, and ship self-serve datasets, data models, and dashboards.
Read the full description (20 more sections)Show less
Work with engineering and research to align pipelines with production constraints (latency, cost, privacy).
Requirements
Strong Python and SQL, with production-grade pipeline engineering (not just notebooks).
Applied NLP/ML on real-world text: embeddings, clustering, topic modeling, semantic search, classification.
Experience at scale: distributed processing, large-scale storage and querying, performance-cost tradeoffs.
Evaluation of fuzzy problems: offline/online metrics, human-in-the-loop labeling, inter-annotator agreement, drift monitoring.
Prior work with safety/moderation datasets, policy/rule systems, or high-volume logging/observability.
Relocation to Paris or London (hybrid) required.
Bonus
Public builder footprint: open-source models, datasets, or frameworks on HuggingFace/GitHub, papers, or technical posts.
Experience at a frontier or near-frontier lab, or leading open-source model releases.
RL for LLMs beyond standard RLHF: online RL, GRPO-style methods.
Moderation, safety, or classification models at scale; multilingual model training.
We offer
Competitive salary + equity.
Hybrid work from central London or Paris office, relocation support for Paris after probation.
Premium private health insurance, mental health support, flexible time off.
Lunch and dinner covered in the office, L&D budget, all hardware and tools you need.
Team off-sites twice a year.
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