(Senior) Material Scientist - ML for Electrochemistry - @Entalpic

👤 Who we are ?

We are a dedicated team at the forefront of AI and chemistry, working to accelerate the energy transition 🌏 We focus on discovering new chemicals and materials that can lead to more sustainable practices in sectors where the need for change is most urgent. To do this, we develop a modern AI-driven discovery platform for new materials &��catalysts that optimize chemical reactions, significantly reducing CO2 emissions 🌱

As an early-stage startup backed by substantial funding (>10M$), we base our approach on state-of-the-art academic research to drive practical business solutions.

🚀 Mission Highlights

We are seeking a Materials Scientist with strong electrochemistry expertise to lead machine learning (ML)-driven materials discovery in fields such as batteries, electrocatalysis, or energy conversion/storage devices. Your role will be to bridge between computational design and experimental validation, ensuring AI-generated candidates are both theoretically promising and practically viable. This includes contributing domain expertise, helping build synthesis-aware ML models, and coordinating with experimental collaborators for testing and iteration.

Your role will involve collaborating with the engineering & science team (~20 people) to enhance materials discovery generative models, optimize active learning workflows, and provide experimental information and validation for Entalpic’s candidate materials. You will focus directly on interfacing AI with synthesis procedure development for materials produced by Entalpic’s generative pipeline. 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 – strong knowledge of the mechanisms and synthesis of novel batteries, electrocatalysts, defects & energy conversion/storage devices, to interface between computational chemists and experimentalists in this field.
• Process-Structure ML models - Build ML models to predict synthesis, characterization, 3D structure and performance of a given material. This includes LLM-based pipelines to extract this data from the chemical/materials science literature.
• AI-driven discovery – Collaborating with AI teams to utilize and enhance generative ML models (e.g. GFlowNet, diffusion), develop active learning strategies and fine-tune ML Interatomic Potential (MLIP) that predict material properties.
• 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 and will be an expert in at least one domain of the synthetic materials science/chemistry literature.
• Scientific communication – Contributing to research publications, patents, and internal reports to ensure knowledge transfer and impact.

🤓 Expertise & skills

• PhD in Chemistry, Material Science or related field.
• Past experience working in a materials science lab, especially in a synthetic materials science domain.
• Previous experience applying ML based workflows to experimental chemistry/materials science.
• 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

• Expertise in quantum chemistry (DFT, semi-empirical methods, MD simulations, QM/MM).
• Experience with synthesis, characterization and testing of materials for electrochemistry applications (e.g. battery electrodes, defects, electrocatalysts) .
• Experience with database engineering.
• Industry experience in material science / battery R&D.

📅 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