ML Research Engineer

Навыки

• API (интеграции)
• Datadog / New Relic
• Дообучение моделей
• GitHub
• Machine Learning
• NLP
• Мониторинг и observability

О компании и продукте

• White Circle https://whitecircle.ai/ is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale.
• We’ve raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and others
• We process over 100M+ API calls every month
• We fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model

Задачи

• 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
• You'll fit right in if you
• Strong Python + SQL with an engineering mindset: you can build reliable pipelines, not just notebooks
• 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
• Comfortable at scale: distributed processing, large-scale storage-querying, and performance-cost tradeoffs
• You 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
• A big plus
• A public builder footprint: open-source models, datasets, or training frameworks on HuggingFace/GitHub, benchmarks, papers (workshop or main conference), or technical posts with real usage
• Experience training models at a frontier or near-frontier lab, or leading open-source model releases with documented adoption
• Experience with RL methods for LLMs beyond standard RLHF: online RL, GRPO-style methods, or novel alignment approaches
• Experience with moderation, safety, or classification models at scale
• Multilingual model training experience
• Why White Circle
• Paid time off in line with your local regulations, no matter where you work from
• Comprehensive medical insurance for our France-based team
• All the hardware, tools, and services you need
• Meaningful equity package

Условия

• Work from Paris (hybrid) with a relocation package available, or work from London (note: we are currently unable to provide relocation support and medical insurance for London-based roles)

Как проходит отбор

• Introductory call with HR (25 min)
• Take-home test task
• Technical interview with Head of Applied Research (60 min)
• Final conversation with our CEO (45 min)
• Please submit your application in English

Паспорт вакансии

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