About the Internship

Are you a passionate and curious engineering student looking for an opportunity to kickstart your career in AI? At Neuralk-AI, we’re seeking a motivated intern to help us evaluate and better understand tabular machine learning models, design robust data and deployment pipelines, and support users in accessing and leveraging the latest model advancements. This is your chance to explore the inner workings of cutting-edge AI systems while gaining hands-on experience in a fast-moving, innovative startup environment!

You will report to Alexandre Pasquiou and will be located in our Paris offices.

About Neuralk

Neuralk is a deep-tech company building the next generation of Foundation Models for Data Science. Our mission is to build the predictive layer for businesses, transforming data science from a series of one-off initiatives, stitched together across silos, overly bespoke, and dependent on a handful of specialists, into a durable capability: a scalable predictive infrastructure that continuously learns from an organization’s data and powers decisions across the enterprise.

Our product is a Data Science agent, powered by our Foundation Models, that assists data scientists throughout their workflow, from problem framing to robust, production-ready models. We focus on the hardest and most common data problems in companies: structured datasets describing customers, operations, risks or financial activity.

As an early-stage, well-funded AI startup, Neuralk builds on state-of-the-art research to solve concrete business challenges. We value clarity over complexity, strong fundamentals over hype, and fast iteration grounded in rigorous engineering. Our ambition is to redefine how predictive AI is built and used in organizations, at scale.

Joining Neuralk means working hard in a fast-moving, research-driven environment, with a high level of ownership and the opportunity to shape a core product at the intersection of machine learning, engineering and real-world impact.

Mission Highlights

As a Data Science Intern, you’ll play a key role in building high-quality training datasets that fuel our AI models. By developing web scraping pipelines and consolidating diverse data sources, you’ll help lay the foundation for groundbreaking advancements in AI for structured data.

Role & Responsibilities

• Collaborative work: Partner with our research and engineering teams (~5 people) to improve model performance, streamline workflows, and shape best practices around tabular modeling.
• Model evaluation & understanding: Analyze and benchmark tabular machine learning models to understand their behavior, strengths, and limitations across different datasets and use cases.
• Pipeline development: Design, implement, and maintain robust data and model pipelines, from data ingestion and preprocessing to model evaluation and deployment workflows.
• Data preparation: Prepare, clean, and structure tabular datasets to ensure reliability, consistency, and reproducibility in experiments and production use.

Minimum Qualifications

• Currently pursuing a degree in Computer Science, Engineering, Data Science, or a related field (Bac+3/Bac+5 or equivalent).
• Programming skills: Proficiency in Python; experience building data or ML pipelines is a strong plus.
• Machine learning foundations: Familiarity with tabular machine learning methods and common libraries (e.g., scikit-learn or similar ecosystems).
• Data handling: Experience with data manipulation and preprocessing tools (e.g., Pandas, NumPy, or similar).
• Strong interest in AI and machine learning, especially in understanding how models behave and how they can be made accessible and reliable for real users.
• Self-starter with the ability to work autonomously and solve problems creatively.
• Good communication skills in English.

Preferred Qualifications

• Experience with large-scale data collection or analysis projects.
• Interest or experience in deep learning frameworks (e.g., PyTorch, TensorFlow).
• Familiarity with version control systems like Git.

Why you should join us ?

• Hands-on learning: Get practical experience in an exciting and rapidly evolving field.
• Mentorship: Work closely with experienced researchers and engineers who are eager to share their knowledge.
• Impactful work: Your contributions will directly support the development of cutting-edge AI models and platforms.
• Dynamic environment: Be part of a fast-growing startup where your ideas and efforts will make a tangible difference.
• Growth opportunities: Gain exposure to advanced AI concepts and methodologies, positioning yourself for a future career in machine learning.