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 willTurn 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).
RequirementsStrong 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.
BonusPublic 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;.