ML Research Engineer - Scale LLMs & AI Safety, Equity

Moonfire; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Hybrid office in London/Paris

Relocation support

Health insurance

Lunch and dinner at the office

Learning and development

Job summary

White Circle is seeking several ML Engineers to train, post-train, and evaluate the LLMs powering our platform. This hands-on role involves building petabyte-scale data pipelines, SFT/RLHF/DPO-style alignment, reward models, and distributed multi-GPU training.

You’ll translate insights into product decisions and create self-serve analytics for the team. The team focuses on safety, reliability, and optimization of AI systems, with opportunities to influence how safety policies are implemented at

Qualifications

• Strong Python + SQL with an engineering mindset.
• Hands-on NLP/ML with real-world text.
• Experience with scale and large data pipelines.
• Ability to evaluate fuzzy problems and monitor drift.

Responsibilities

• Turn petabytes of unstructured text into a structured, explorable view.
• Build scalable representation pipelines, embeddings at scale, indexing, retrieval.
• Use LLMs for labeling, supervision, data enrichment, and diagnostics.
• Deliver insights that translate into product and operational actions.
• Ship self-serve analytics with datasets, models, and lightweight dashboards.
• Partner with engineering to align pipelines with production constraints.

Skills

Python

SQL

NLP/ML

Distributed processing

Embeddings

Clustering

Topic modeling

Semantic search

Classification

Data engineering

Tools

HuggingFace

GitHub

Job description

White Circle is seeking several ML Engineers to train, post-train, and evaluate the LLMs powering our platform. This hands-on role involves building petabyte-scale data pipelines, SFT/RLHF/DPO-style alignment, reward models, and distributed multi-GPU training.

You’ll translate insights into product decisions and create self-serve analytics for the team. The team focuses on safety, reliability, and optimization of AI systems, with opportunities to influence how safety policies are implemented at