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ML Research Engineer

White Circle· Research
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Hybrid (Paris), Hybrid (London)Full-timePosted 3mo ago

About the role

ML Engineer focused on training, evaluating, and improving LLMs at the core of White Circle's platform, with hands-on modern model training, large-scale data pipelines, alignment methods, and distributed multi-GPU training.

Responsibilities

  • 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.

Qualifications

  • 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

Skills

Required:PythonSQLNLP / ML on real-world textDistributed processingEvaluation & experimentation
Nice to have:Safety/moderation datasetsRL methods for LLMsMultilingual model training

Benefits

  • Competitive compensation, including equity
  • Flexible time off
  • Office in central London/Paris with flexible hybrid setup
  • Relocation support after probationary period
  • Premium private health insurance
  • Mental health support, including therapy coverage
  • Lunch and dinner when in the office
  • Learning and development support for courses, conferences, growth opportunities
  • All the hardware, subscriptions, tools, and services you need
  • Team off-sites twice a year (e.g., Alps, Saint-Tropez, Marbella)
Last updated Sep 30, 2026. Interested? Apply on the company site.
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About White Circle

We keep your AI models safe, reliable, and secure.

We keep your AI models safe, reliable, and secure.

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