LLM Safety ML Engineer — Scalable Training & Alignment

White Circle; Paris; Hybrid; EUR 90,000 - 140,000; Full time

Benefits offered by this job

Competitive compensation including equ

Equity

Flexible time off

Hybrid office in Paris/London

Relocation support to Paris

Private health insurance

Mental health support

Meals when in office

Learning & development support

Hardware & tools provided

Team off-sites

Job summary

White Circle is seeking several ML Engineers to train, post-train, and evaluate LLMs at the core of our platform. You will handle large-scale data pipelines, SFT/RLHF/DPO-style alignment, reward models, and distributed multi-GPU training.

Join a small, focused team building safety, reliability, and optimization layers for AI systems. You will ship experiments to production quickly and influence how AI safety is actually built.

Qualifications

• Strong Python + SQL with an engineering mindset to build reliable pipelines (not just notebooks).
• Solid applied NLP/ML experience on real-world text: embeddings, clustering, topic modeling, semantic search, classification; understand failure modes and debugging.
• Experience at scale: distributed processing, large-scale storage-querying, and performance-cost tradeoffs.
• Ability to evaluate fuzzy problems: offline/online metrics, human-in-the-loop labeling, drift monitoring, reproducibility.
• Prior work with safety/moderation datasets, policy/rule systems, or high-volume logging/observability.

Responsibilities

• Turn petabytes of unstructured text into a structured, explorable view (topics, clusters, segments, trends, anomalies).
• 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.

Skills

Python

SQL

NLP/ML

Embeddings

Topic modeling

Semantic search

Distributed processing

Evaluation metrics

Human-in-the-loop

Safety datasets

Moderation

Job description

White Circle is seeking several ML Engineers to train, post-train, and evaluate LLMs at the core of our platform. You will handle large-scale data pipelines, SFT/RLHF/DPO-style alignment, reward models, and distributed multi-GPU training.

Join a small, focused team building safety, reliability, and optimization layers for AI systems. You will ship experiments to production quickly and influence how AI safety is actually built.