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# Scientific Software Engineer, AI for Materials - @Entalpic

- **Company:** Breega
- **Location:** Paris, France (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Information Engineering, Data Files, Experimental Data, Information Extraction, Python (Programming Language), Software Engineering, Property Testing, Large Language Models, Build Management, Software Version Control, Data Pipelines
- **Published:** September 25, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3ac9f6953b4a8e3e

## About the Role

* M.S. or PhD in Chemistry, Materials Science, Chemical Engineering, or a related field.
 * 2+ years of professional experience in software engineering, data engineering, or scientific software development.
 * Strong understanding of chemistry and/or materials science, with the ability to reason about chemical compounds, physical properties, and experimental data.
 * Strong Python software engineering skills, including testing, version control, and designing maintainable systems.
 * Experience working with complex scientific or experimental data.
 * Excellent communication skills in English and ability to work effectively across scientific and engineering teams.
 * Thrives in a fast-paced, evolving startup environment., * Experience with thin-film deposition processes (ALD, ALE, ASD), inorganic molecules or device qualification metrics.
 * Experience with LLMs, agentic systems, or scientific information extraction.
 * Familiarity with cheminformatics, chemical structures and identifiers, or tools such as RDKit.

## Description

You will report to the Experimental Data Team Lead of Entalpic and will be located in our Paris offices., We are looking for a Scientific Software Engineer with a strong background in chemistry and/or materials science to build the scientific data platform powering Entalpic's AI-driven materials and molecules discovery platform.

This is a software engineering role where scientific expertise is essential. You will work at the intersection of chemistry, scientific data, and AI, turning scientific literature, patents and experimental lab data into high-quality structured datasets.

Your initial focus will cover two closely related areas:

 * Advanced materials and thin-film properties, from precursor chemistry to device-relevant characteristics (electronic, optical, etc.)..
 * Atomic layer manufacturing processes, including Atomic Layer Deposition (ALD), Atomic Layer Etching (ALE), Chemical Vapor Deposition (CVD) and Area-Selective Deposition (ASD).

Your mission will be to define how scientific information should be represented, build AI-powered extraction and processing pipelines, curate and annotate high-quality scientific data, and develop tools for chemical identity resolution and data quality. Your goal will be to enable the Computational Chemistry and ML teams with large high-quality data sets.

Role & responsibilities

 * Scientific Data Modeling: Use your chemistry and materials science expertise, supported by domain experts, to understand the scientific problems and define clear schemas and extraction specifications for chemical properties, materials, and experimental processes.
 * Agentic Scientific Extraction: Design and build agentic workflows using LLMs and scientific tools to extract structured information from scientific literature and experimental data.
 * Data Quality & Evaluation: Build robust quality mechanisms for scientific datasets, including chemical identity resolution, automated evaluations, consistency checks, provenance, and targeted human review of difficult or uncertain cases.
 * Scientific Data Engineering: Build and maintain reliable, tested, and scalable Python pipelines that transform heterogeneous scientific sources into high-quality datasets and contribute to the architecture of Entalpic's scientific data platform.
 * Cross-team Collaboration: Work closely with domain experts, computational chemists, materials scientists, and AI engineers to translate scientific needs into data specifications, software, and datasets that can directly support modeling and discovery.