npv labs

ML Research Engineer

Natural Language Processing & LLMsConfirmed live in the last 24 hours

Full-time

About the Job

Requirements

Strong Python and SQL skills, with production-grade pipeline engineering experience rather than notebook-only work.
Applied natural language processing and machine learning experience on real-world text, including embeddings, clustering, topic modeling, semantic search, and classification.
Experience with distributed processing, large-scale storage and querying, and performance-cost tradeoffs.
Experience evaluating fuzzy problems using offline and online metrics, human-in-the-loop labeling, inter-annotator agreement, and drift monitoring.
Prior work with safety or moderation datasets, policy or rule systems, or high-volume logging or observability.
Must relocate to Paris or London; the role is hybrid.

Responsibilities

Turn petabytes of unstructured text into a structured, explorable view of topics, clusters, segments, trends, and anomalies.
Build scalable representation pipelines for sampling, preprocessing, embeddings at scale, indexing, and retrieval.
Use large language models for labeling, weak supervision, data enrichment, and automated diagnostics, with cost and quality controls.
Translate findings into product and operational decisions, and ship self-serve datasets, data models, and dashboards.
Work with engineering and research teams to align pipelines with production constraints including latency, cost, and privacy.

Desired Qualifications

Public builder footprint through open-source models, datasets, or frameworks on Hugging Face or GitHub, papers, or technical posts.
Experience at a frontier or near-frontier lab, or experience leading open-source model releases.
Reinforcement learning for large language models beyond standard reinforcement learning from human feedback, including online reinforcement learning or GRPO-style methods.
Experience with moderation, safety, or classification models at scale, or multilingual model training.

We're looking for ML Engineers to join White Circle, an AI Safety company building the safety, reliability, and optimization layer for AI systems through natural-language policies it automatically tests, enforces, and improves at scale. Backed by $70M (Series A) from top funds and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, and others, White Circle processes 100M+ API calls monthly and fine-tunes and trains its own LLMs to run faster and cheaper than open or proprietary models.

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.

About the Company

npv labs

Company size: Not listed
Company stage: Not listed
Total funding: Not listed
Headquarters: Not listed
Founded: Not listed

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