ML Research Engineer

Npv; Paris; Hybride; EUR 90 000 - 130 000; Plein temps

Avantages offerts par ce poste

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

Résumé du poste

White Circle is seeking ML Engineers to join its AI Safety team building policy enforcement and optimization layers for AI systems. Hybrid work from Paris or London, with relocation support after probation.

The role focuses on turning petabytes of unstructured text into structured insights, building scalable pipelines, and applying LLMs for labeling and diagnostics. You will work with engineering and research to align pipelines with production constraints, delivering self-serve datasets, data

Qualifications

• Strong Python and SQL, with production-grade pipeline engineering.
• Applied NLP/ML on real-world text: embeddings, clustering, topic modeling, semantic search, classification.
• Experience at scale: distributed processing and large-scale storage and querying.

Responsabilités

• Turn petabytes of unstructured text into a structured, explorable view across topics, clusters, segments, trends and anomalies.
• Build scalable representation pipelines: sampling, preprocessing, embeddings, 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.

Connaissances

Python

SQL

NLP/ML

Distributed processing

MLOps

Description du poste

We're looking for ML Engineers to join White Circle , an AI Safety company building the policy enforcement and optimization layer for AI systems. Backed by $11M from senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, and DeepMind, White Circle processes 100M+ API calls monthly and runs its own LLMs in production. 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. 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