About Entalpic

We are a dedicated team at the forefront of AI and chemistry, working to accelerate the energy transition. Our focus is on discovering new chemicals and materials that can lead to more sustainable practices in sectors where the need for change is most urgent. Specifically, we develop a modern Generative AI platform to discover new catalysts to optimize reactions that significantly reduce CO2 emissions, aiming to make a substantial impact on the environment.

As an early-stage startup with strong funding, we base our approach on state-of-the-art academic research to drive practical business solutions. We value clear communication and simplicity in our approaches, promoting a constant optimization mindset.

Join Entalpic to be part of a growing team, eager to learn and adapt, united by the belief that our technology can make a significant positive impact and contribute to transforming carbon-intensive industries for a sustainable future.

Mission Highlights

As a Data Engineer Intern at Entalpic, you will join the Platform team and contribute to the core software foundations of our data stack while learning how data engineering and software engineering practices enable advanced AI and scientific workflows. Your mission will be to help design, implement, and maintain a robust, scalable, and maintainable data infrastructure and internal data platform. You will work on building and integrating data pipelines that ingest diverse sources such as simulation outputs, experimental results, scientific papers, and patents. In this role, you will apply software engineering principles including modular design, testing, documentation, and code quality to improve data accessibility, reliability, and usability for researchers, data scientists, and engineers across the company.

Role & responsibilities

1. Data Engineering Implementation: Help build and maintain data pipelines that process and integrate multimodal data including simulation results, scientific text, and experimental measurements.

2. Data Storage and Organization: Contribute to the implementation and organization of data storage solutions, ensuring data is well-structured, accessible, and documented.

3. Automation and Tooling: Write scripts to automate data ingestion, cleaning, and transformation tasks under the guidance of senior engineers.

4. Data Visualization and Analytics: Support the creation of basic data visualizations and analytical dashboards to help teams explore datasets, monitor data quality, and gain insights from experimental, simulation, and textual data.

5. Applied ML Research: Prototype end-to-end pipelines using state-of-the-art LLMs for chemical entity extraction and explore ranking methods to improve data quality and retrieval for downstream AI systems.

6. Data Quality and Documentation: Assist with data validation, basic lineage tracking, and documentation to improve data reliability and reproducibility.

7. Infrastructure Collaboration: Work with data engineers, data scientists, and DevOps to understand how data systems are deployed and scaled on cloud platforms such as AWS or GCP.

8. Cross-team Collaboration: Interact with scientists and engineers to understand data needs and help translate them into practical data solutions.

Profile

Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Data Engineering, Software Engineering, or a related field.

• Strong interest in data engineering, data systems, and infrastructure for machine learning or scientific applications.

• Proficiency in Python and familiarity with at least one other programming language is a plus.

• Foundational knowledge of SQL and relational databases, with curiosity about NoSQL systems.

• Understanding of core data concepts such as ETL, data modeling, and data pipelines.

• Must be comfortable working in English in a technical environment.

• Motivated, curious, and eager to learn in a fast-paced startup setting.

Nice to Have

• Exposure to cloud platforms such as AWS or GCP through coursework, projects, or internships.

• Interest in machine learning, AI systems, or scientific computing.

• Familiarity with Git and collaborative software development workflows.

• Familiarity with containerization tools such as Docker.

• Interest in materials science, chemistry, or physics is a strong plus but not required.

What you will learn

• How to design and operate data pipelines in a real-world AI and scientific context.

• Best practices for building reliable, scalable data infrastructure in a startup environment.

• How data engineering supports machine learning, LLMs, and scientific discovery.

• How to collaborate effectively across engineering, data science, and research teams.

Recruitment Process

• A 45min interview with a platform team manager

• A take-home coding exercise with a one hour debriefing

Compensation & benefits

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

• Competitive salary + full reimbursement of your transport card

• Flexible work environment: Remote-Friendly with a minimum of 3 days in Paris offices per week (Station F)

• 1 paid day off per month

• Professional development opportunities: access to conferences and internal learning sessions

Department

Join our startups

Locations

Paris

Remote status

Hybrid

Employment type

Internship