Junior AI Scientist for Materials

Requirements

• Strong Python programming with clean, structured coding practices
• Web application frameworks: Streamlit, FastAPI, Flask, or similar
• LLM/RAG fundamentals: embeddings and vector databases (ChromaDB, Pinecone, FAISS), prompt engineering and chain-of-thought reasoning, API integration with LLM providers (OpenAI, Azure OpenAI, Claude, etc.)
• Data processing & analysis: pandas, numpy, data cleaning, EDA, visualization (matplotlib, plotly, seaborn)
• Version control: Proficient with git workflows (branching, PR, code review)
• Good to Have:
• Experience working with chemistry/materials data (chemical structures, molecular properties, polymer characterization data, formulation compositions)
• Knowledge of cheminformatics libraries (RDKit for molecular handling, mordred for descriptor calculation)
• Familiarity with machine learning for molecules/materials (QSAR/QSPR, property prediction, molecular fingerprints)
• Understanding of polymer chemistry fundamentals (monomer-polymer relationships, polymerization mechanisms, structure-property principles)
• Cloud deployment experience (Azure, AWS, containerization with Docker)
• Knowledge of laboratory digitalization, electronic lab notebooks (ELN), and FAIR data principles
• Soft Skills & Mindset
• Product thinking : Ability to define problems clearly, break complex R&D challenges into actionable steps, think from chemist's user perspective, prioritize features based on impact
• R&D domain curiosity : Genuine interest in chemistry and materials science; willingness to learn polymer structures, formulation principles, and characterization techniques by observing lab workflows and talking to chemists
• Engineering mindset : Care about code maintainability, documentation quality, scalability, and production-grade development practices
• Collaboration : Comfortable w

Benefits

Additional Information

We are Covestro. We are curious. We are courageous. We are colorful. We refine chemical material solutions with game-changing products. Let us empower you to push boundaries. Join us and our 18.000 colleagues now and together we will make the world a brighter place. MAJOR TASKS & RESPONSIBILITIES Design, build and deploy LLM/RAG applications addressing specific materials R&D workflows: polymer literature analysis and synthesis route discovery, formulation design assistant, experimental data extraction and structuring, technical documentation generation, and property-structure relationship exploration Work embedded with polymer chemists and formulation scientists to understand polymerization processes, coating/adhesive formulations, and material characterization workflows; identify pain points and translate them into structured AI use cases with clear problem statements and success metrics Implement production-grade AI solutions using modern frameworks (Streamlit, FastAPI, Gradio) with embeddings, vector databases, and prompt engineering techniques; integrate chemistry-specific knowledge (SMILES, polymer structures, material properties, formulation parameters) into AI models Build and maintain data pipelines for materials data: chemical structure parsing, property data cleaning, experimental result extraction from reports, feature engineering for polymer descriptors, and visualization of structure-property relationships Define user journeys for R&D scientists working on polymer synthesis, formulation optimization, and material testing; prioritize features based on R&D impact and feasibility through small iterative milestones and continuous user feedback Apply data science best practices to materials datasets: exploratory data analysis of formulation spaces, statistical analysis of experimental results, visualization of polymer property trends, and basic predictive modeling for material performance Ensure code quality through clean architecture, modularization, proper git workflows, comprehensive documentation, and engineering mindset focused on maintainability and scalability Stay current with AI for science developments (materials informatics, molecular LLMs, generative chemistry models); contribute to APAC Digital R&D capability building through knowledge sharing, tutorials, and best practice documentation Collaborate with computational chemistry and materials modeling teams to integrate AI-driven approaches with physics-based simulations for polymer design and formulation optimization Engage with external AI/materials research communities, universities, and technology providers to track emerging methodologies; support cross-BE digital R&D projects and contribute to IP strategy for AI-generated materials insights REQUIRED QUALIFICATIONS Education & Experience Master's degree in Computer Science, Data Science, Chemistry, Materials Science, Chemical Engineering, Polymer Science, or related fields 1-3 years of experience in data science, AI application development, or computational R&D Demonstrated experience building at least one complete LLM/RAG application from concept to deployment Technical Skills

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