Our company: Entalpic

We are a dedicated team at the forefront of AI, chemistry and materials science, building new ways to engineer materials and manufacturing processes at the atomic scale. We combine machine learning, computational chemistry and multi-scale physics with experimental validation to discover new materials and optimize how they are made. Our initial focus is on the semiconductor industry, where we work across thin films, interfaces and atomic-scale processes such as ALD and ALE, while continuing to develop applications in catalysis, batteries and photovoltaics.

As an AI-driven deep tech company backed by significant funding ($10M), we build on state-of-the-art research to solve real industrial challenges. Our ambition is to connect materials discovery, process optimization and device performance in a single integrated platform, working closely with leading industrial and research partners. In the long term, we believe better materials and more efficient manufacturing processes can play a significant role in reducing the energy and environmental footprint of some of the world’s most critical industries.

Join Entalpic to be part of a growing multidisciplinary team of machine learning researchers, computational scientists, chemists and experimentalists. You will work at the intersection of fundamental science and industrial innovation, with the opportunity to shape both our technology and how some of the world’s most advanced materials and devices are developed. We value scientific rigor, clear communication and simplicity, with a constant drive to learn, iterate and improve.

Co-founders: Mathieu Galtier, Victor Schmidt, Alexandre Duval

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 this job description’s requirements. If you have passion for our mission and believe you can contribute, we want to hear from you.

Reporting & Job Location

You will report to the Experimental Data Team Lead of Entalpic and will be located in our Paris offices.

Mission Highlights

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.

Profile

• 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.

Nice to have

• 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.

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:

• A competitive salary

• Equity (BSPCE), to reflect the value you bring to Entalpic and to foster a shared journey

• Comprehensive health insurance (Alan blue)

• French level paid leave and time-off work

• Dynamic work setting. Although our preference is for in-person collaboration, we will be flexible with occasional remote work arrangements.

• and more to come as we grow