ML Research Engineer

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

Compensation & benefits

• Competitive compensation, including equity

• Flexible time off

• Office in central London/Paris with flexible hybrid setup

• Relocation support if you’re moving to Paris, available after your probationary period

• Premium private health insurance

• Mental health support, including coverage for therapy when you need it

• Lunch and dinner covered when you work from the office

• Learning and development support for courses, conferences, and opportunities to grow your skills

• All the hardware, subscriptions, tools, and services you need

• Team off-sites twice a year: we’ve recently been to the Alps, Saint-Tropez, and Marbella