ML Research Engineer – Hybrid (Paris/London)

Npv; Paris; Hybrid; EUR 65,000 - 90,000; Full time

Benefits offered by this job

Relocation support

Hybrid work (Paris/London)

Healthy & mental health support

Job summary

White Circle is seeking ML Engineers to build safety, reliability, and optimization layers for AI systems. You will design scalable NLP pipelines, embeddings, and detection workflows, collaborating with engineering and research to meet latency and privacy constraints.

The role requires production-grade pipeline engineering, experience with large-scale data, and hands-on work with embeddings, topic modeling, and semantic search.

Qualifications

• 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, drift monitoring.

Responsibilities

• 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).
• Experience at scale: distributed processing, large-scale storage and querying, performance-cost tradeoffs.

Skills

Python

SQL

NLP

embeddings

Tools

HuggingFace

GitHub

Job description

White Circle is seeking ML Engineers to build safety, reliability, and optimization layers for AI systems. You will design scalable NLP pipelines, embeddings, and detection workflows, collaborating with engineering and research to meet latency and privacy constraints.

The role requires production-grade pipeline engineering, experience with large-scale data, and hands-on work with embeddings, topic modeling, and semantic search.