ML Engineer (AI)

Location: Hybrid, with offices in Paris and London. Relocation support is available for moving to Paris after the probationary period.

Company

hirify.global is an AI Safety company building a safety, reliability, and optimization layer for AI systems through natural-language policies that are tested, enforced, and improved at scale.

What you will do

• Transform petabytes of unstructured text into structured views of topics, clusters, segments, trends, and anomalies.
• Build scalable pipelines for sampling, preprocessing, normalization, embeddings, indexing, and retrieval.
• Apply LLMs to labeling, classification, weak supervision, data enrichment, summarization, and automated diagnostics.
• Turn data findings into product and operational decisions, including identifying quality gaps and priorities.
• Develop self-serve datasets, data models, analytics tools, and lightweight dashboards.
• Partner with engineering and research to integrate pipelines into production workflows while managing latency, cost, and privacy constraints.

Requirements

• Strong Python and SQL skills with an engineering approach to building reliable pipelines.
• Applied NLP and ML experience with embeddings, clustering, topic modeling, semantic search, and classification.
• Experience with distributed processing, large-scale storage and querying, and performance-cost tradeoffs.
• Ability to evaluate ambiguous problems using offline and online metrics, human-in-the-loop labeling, inter-annotator agreement, drift monitoring, and reproducibility practices.
• Experience with safety or moderation datasets, policy or rule systems, or high-volume logging and observability.

Nice to have

• Open-source models, datasets, training frameworks, benchmarks, papers, or technical posts with real usage.
• Experience training models at a frontier or near-frontier lab or leading open-source model releases.
• Experience with advanced RL methods for LLMs, including online RL and GRPO-style methods.
• Experience with moderation, safety, or classification models at scale.
• Multilingual model training experience.

Culture & Benefits

• Small, focused team working on difficult AI safety problems with rapid production delivery.
• Competitive compensation with equity and flexible time off.
• Premium private health insurance and mental health support, including therapy coverage.
• Lunch and dinner covered when working from the office.
• Support for courses, conferences, hardware, subscriptions, tools, and professional development.
• Team off-sites twice a year.

Hiring process

• Introductory call with the Talent Team.
• Test assignment followed by a technical interview with the Head of Applied Research.
• Final conversation with the CEO.