Stage IA/ML Engineer 100% R&D - agentic AI
Overview
Ekimetrics offers a six-month research and development internship for a Stage IA/ML Engineer focused on agentic AI, based in Paris, France. The Innovation Department works on AI research topics in collaboration with industrial and academic partners, aiming to close the gap between cutting-edge AI agents and the day-to-day reality of delivering data science at scale. The intern will work with senior data scientists and AI builders to build and refine remit, a harness that semi-automates the data science workflow from a business question to a trusted recommendation.
Who can apply
Candidates must currently be in the final year of a Master's or Engineering degree, with strong foundations in machine learning, statistics, and programming. Applicants require a good grasp of model validation, including train/test splits, data leakage, and baselines. Additional requirements include strong analytical and problem-solving skills, familiarity with large language models and agentic techniques, strong Python skills, a solid command of the standard data science toolkit, and fluency in English. Experience with an agent framework, software engineering practices, and exposure to Databricks, MLflow, or cloud data platforms are considered helpful assets.
What you get
The internship provides hands-on research and engineering experience building a pioneering agentic system on real data science workflows. Participants engage with a team of data scientists, AI experts, and PhDs in a dynamic, research-driven environment, gaining expertise in LLM agents, AI system evaluation, and production tooling on Databricks. Successful completion of the internship may offer an opportunity to transition into a full-time Data Scientist role in AI at Ekimetrics or into a PhD program, along with the potential to co-author and publish scientific papers in reputable AI and data science conferences and journals. Visa sponsorship may be offered for suitable candidates.
Key dates
The internship lasts for a duration of six months.
Links in this posting
- [1] LLM-Based Data Science Agents: A Survey of Capabilities, Challenges, and Future Directions (2025). https://arxiv.org/abs/2510.04023
- [2] MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering (2025). https://arxiv.org/abs/2410.07095
- [3] Ambig-DS: A Benchmark for Task-Framing Ambiguity in Data-Science Agents (2026). https://arxiv.org/abs/2605.09698
- [4] TabICLv2: A Better, Faster, Scalable, and Open Tabular Foundation Model (2026). https://arxiv.org/abs/2602.11139