# ML Research Engineer at Whitecircle

- Company: Whitecircle (whitecircle.com)
- Location: Paris (remote)
- Places: Paris, Remote, France
- Posted: 2026-07-02
- Apply: https://jobs.ashbyhq.com/whitecircle/49c8b015-2d99-4173-b9a7-b4a0a0316eff
- Page: https://ye.saranatour.dev/jobs/ml-research-engineer-whitecircle-kn7cgm1qsytnwwbcdkedk4hwms8hdxcq

## The posting

ML Research Engineer
Whitecircle · Paris

**TL;DR:**We are looking for several ML Engineers to train, post-train, and evaluate the LLMs at the core of our platform. This is hands-on modern model training work: large-scale data pipelines, SFT/RLHF/DPO-style alignment, reward models, distributed multi-GPU training, and evaluation.

**About us**

[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 recently raised our Series A funding round, taking our total funding to $70M. Our investors include 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

We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need.

**What you’ll do**

- 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**

- 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

**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
- Exper