ML Research Engineer

Npv·Parismid

In short

→Ingeniero de investigación en ML que transforma texto sin estructura en insights accionables a escala.
→Diseña y mantiene pipelines de procesamiento de datos a gran escala con foco en seguridad y eficiencia.
→Destaca el rol como parte de un equipo de inteligencia artificial de seguridad con backing de líderes de la industria.

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What they ask for

✓Python y SQL avanzados con experiencia en pipelines en producción.
✓Experiencia aplicada en NLP/ML: embeddings, clustering, topic modeling, búsqueda semántica.
✓Capacidad de trabajar con datos a gran escala: procesamiento distribuido, almacenamiento y rendimiento.
✓Evaluación de problemas ambiguos con métricas offline/online y supervisión humana.
✓Experiencia previa con datasets de seguridad, moderación o sistemas de políticas.
✓Disponibilidad para relocalización a París o Londres (híbrido).

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PythonSQLDistributed ProcessingLarge-Scale StorageQueryingEmbeddingsIndexingRetrievalLLMsSemantic Search

Who should you write to at Npv?

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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. Find more English Speaking Jobs in France on Arbeitnow

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