About Arlequin

Arlequin is building the sovereign European alternative to Palantir: an AI platform capable of processing massive data volumes without bias, with high-impact use cases such as fighting disinformation. Founded in late 2024 by Hugo Micheron (researcher in geopolitics and systemic risk) and Antoine Jardin (CNRS research engineer).
~30 people, post-seed, Series A in progress. On-site in Paris.

The role

You'll join our R&D team of 8, focused on researching, testing, and developing state-of-the-art algorithms that power HUDEX, our platform that lets non-technical analysts interpret narrative dynamics across large-scale digital ecosystems. You'll work alongside experienced researchers, several of whom hold or are completing PhDs, across a range of fields (unsupervised learning, NLP, computer vision, topology, and more).

Stack

• Technical stack:

  • Must have: Python and strong experience in data manipulation / data science / machine learning / development tools and libraries, Git/GitHub

  • Nice to have: LaTeX, MLflow

• Methods & domains:

  • Must have: experience in NLP, working with high-dimensional unstructured data (text, computer vision, speech, multimodality…), clustering and statistics

  • Nice to have: experience with other unsupervised learning methods, deep learning, mathematical knowledge in graph and/or topology

What we're looking for

• A strong scientific mindset and genuine intellectual curiosity
• Able to read scientific literature and produce literature reviews
• A high degree of autonomy
• Versatility and adaptability: you're comfortable researching across many fronts, switching between quick, pragmatic solutions and deeper long-term work, and occasionally stepping outside pure research to support engineering or client-facing needs
• Quick to pick up new algorithms and put them into practice
• Open to junior candidates
• Professional English

Bonus

• Academic publications

• Already comfortable in a startup environment

• Mastery of agentic coding tools and code models

Process (~2 weeks)

1. Screening (15 min)

2. Fit interview (30 min)

3. Technical interview (1h)

4. Founder culture fit (45 min)

5. Reference calls

6. Offer