Materials Science Intern : ML for Structure-Process Relationships @Entalpic

Breega; Paris; Hybride; EUR 60 000 - 80 000; Plein temps

Résumé du poste

Breega is seeking a Materials Science Intern to lead machine learning-driven material discovery for catalytic reactions in Paris. This role involves collaborating with a dynamic team on enhancing materials discovery pipelines, bridging the gap between AI design and real-world applications, while offering a hybrid work environment and professional development opportunities.

Prestations

Flexible work environment

1 paid day off per month

Full reimbursement of transport card

Access to conferences and internal learning sessions

Qualifications

• Experience with wet-lab materials science procedures.
• Solid experience in building data & ML workflows in an experimental context.
• Strong motivation to work in a fast-paced startup environment.

Responsabilités

• Identify and prioritize materials systems for AI-driven discovery.
• Build LLM based pipelines for data extraction from literature.
• Engage with experimentalists to ensure testability of discoveries.

Connaissances

Analytical skills

Problem-solving

Communication skills

Formation

MSc or PhD in Chemistry, Chemical Engineering, Material Science, Machine Learning

Outils

Python

Git

PyTorch

Description du poste

We are seeking a Materials Science Intern to lead machine learning (ML)-driven material discovery for catalytic reactions. This position will focus on developing an ML pipeline interfacing between theoretical and experimental materials science, specifically linking : 3D crystal structure >

synthesis procedure >

3D structure. You will work at the interface of ML, computational chemistry, and experimental validation, ensuring of the chemical relevance of the data gathered and the ML models output.

Your role will involve collaborating with the engineering & science team (~20 people) to enhance Entalpic’s materials discovery pipeline, building models which connect simulated discoveries to the real world and providing experimental validation for Entalpic’s candidate materials. You will focus directly on interfacing open-source and AI-generated datasets of 3D structures with synthesis procedure development and characterization data. In so doing, you will work alongside experimental partners and industry clients to ensure that our computational discoveries have a viable path to experimental validation, bridging the gap between AI-driven design and real-world applications.

Role & responsibilities

This position directly supports the company’s mission of discovering materials to optimize carbon intensive industries. You will be responsible for :

• Domain expertise – Identifying and prioritizing materials systems where AI-driven discovery can accelerate innovation. The ideal candidate will have the domain knowledge of materials science necessary to interface between computational chemists and experimental materials scientists.
• Chemical literature parsing - Building LLM based pipelines to extract synthesis, characterization and performance values from the chemical / materials science literature. Combine domain knowledge in this space with a strong foundation in programming to be an active member of an LLM based extraction team.
• ML models build models to predict material synthesizability, generate associated synthesis recipes, predict characterization data, and map synthesis procedure and characterization back to 3D structures.
• Collaboration with experimentalists – Engaging with wet-lab researchers to ensure AI-driven discoveries are synthesizable and testable, iterating based on real-world feedback. The ideal candidate will have experience with synthetic materials science / chemistry.
• Scientific communication – Contributing to research publications, patents, and internal reports to ensure knowledge transfer and impact.

Expertise & skills

• MSc and / or
• enrolled in a PhD in Chemistry, Chemical Engineering, Material Science, Machine Learning or a related field.
• Proficiency with wet-lab materials science procedures.
• Solid experience in building data & ML workflows in an experimental materials science / chemistry context
• Proficiency in Python, Git, LLMs, and ML frameworks (PyTorch).
• Strong analytical skills, problem-solving ability, and motivation to work in a fast-paced startup environment.
• Excellent communication skills in English and ability to disseminate science to others.

Bonus Skills

• Past experience working in a materials science lab (industry R&D is a big plus), especially in a synthetic materials science domain.

Recruitment Process

• Interview with the hiring manager
• Technical interview about material science, catalysis and machine learning
• Coding interview
• Final interview with the C(cience)O

Reporting & Job Location

You will report to the Chief Science Officer ( CSO ) of Entalpic and will be located in our Paris offices, with up to 2 days of remote work per week.

We are a no-nonsense startup, where we favor a sustainable culture promoting work-life balance and good compensation over football tables and free food. We offer :

• A dynamic and flexible work environment : Hybrid-Friendly with a minimum of 3 days in Paris offices per week (Station F)
• 1 paid day off per month
• Full reimbursement of your transport card
• Professional development opportunities : access to conferences and internal learning sessions

Entalpic is dedicated to equal opportunity employment and fosters an environment that is open and respectful of diversity. All applicants are encouraged to apply, even if you don’t meet all above requirements. If you have passion for our mission and believe you can contribute, we want to hear from you.

Information

• Duration : 4-6 months
• Start : From September 2025

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