Machine Learning Engineer Property Prediction @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 that optimize chemical reactions, significantly reducing CO2 emissions and thus making a substantial impact on the environment. As an early-stage AI-driven startup backed by significant funding (>5m), 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. 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 above 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 CTO of Entalpic and will be located in our Paris offices. Mission Highlights As a Machine Learning Engineer, your role will be to translate complex ML algorithms for materials property prediction from conceptual frameworks into robust, scalable models within our platform. You will collaborate closely with our research and engineering teams (~10 people) to enhance the performance, scalability and impact of our AI-driven solutions. Role & responsibilities By contributing to the core of our discovery platform, this position directly supports the company’s mission of discovering materials to optimize carbon intensive industries. You will be responsible for: Algorithm development: implement, evaluate and optimize machine learning models for atomic graphs based on cutting-edge research and internal insights. Ensure that they are efficient and well-integrated into our platform. Model Integration and management: Oversee the full lifecycle of machine learning models, including data collection, integration, versioning, and maintenance within our platform infrastructure. Monitoring and debugging: efficiently track the performance of live models and develop debugging tools to swiftly resolve any issues. Reproducibility and traceability: Establish systems and practices ensuring that all experiments and models are reproducible and traceable. Research: Stay current with the latest ML advancements in the field and suggest integrations that may improve the platform performance and capabilities. Collaboration: Work closely with data scientists, chemists, and other engineers to understand requirements and deliver solutions that enhance our research capabilities. Profile PhD or M.S in Computer Science, Machine Learning or a closely related field, with a focus on deep learning. 3+ years of experience in machine learning and software engineering-related positions which involved training DL algorithms (GNN, Transformers) in the cloud. Excellent communication skills in English. Proven ability to work with interdisciplinary teams. Thrives in a fast-paced, evolving startup environment. Bonuses: You have a publication record in top-tier ML conferences or journals You have demonstrated experience in designing and running large-scale ML experiments. You have already played with materials datasets. Expertise Machine Learning: Deep understanding of ML theories and practices, especially related to reproducibility and scalability. (Geometric) Graph Neural Networks: Experience in developing, training and evaluating GNNs for 3D atomic systems (molecules, proteins, materials, etc.). Programming: Proficient in Python, with experience in software development best practices and version control systems such as Git. Data management: Familiarity with data structures and database systems, both SQL and NoSQL, to manage and process large datasets efficiently. AI platforms: Experience with deploying and managing machine learning models, including familiarity with Pytorch and containerization technologies (e.g., Docker, Kubernetes). 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 #J-18808-Ljbffr