Intern, Machine Learning Research Scientist

What You’ll Do

Our team is at the forefront of applying machine learning to real-world organic material research. As part of a dynamic group of researchers, engineers, you’ll collaborate on innovative projects that transform data into insights and breakthrough discoveries of new material. Over the summer, you'll immerse yourself in an environment that values creativity, collaboration, and cutting-edge experimentation—working alongside experts who are as passionate about technology and science as you are

• Project Overview: Artificial Intelligence for Science in Computational Organic Material
• Skills You’ll Learn:
  • Machine Learning & AI: Gain hands-on experience designing, implementing, and validating algorithms in the context of computational organic materials.
  • Data Analysis & Simulation: Develop proficiency in data preprocessing, feature engineering, and simulation platforms essential for modeling complex molecular behaviors.
  • Collaborative Research: Learn effective collaboration with multidisciplinary teams, engaging with experts from research, engineering, and academic backgrounds to solve challenging scientific problems.

Location: Hybrid, working onsite at our San Jose office/headquarters 3-5 days per week, with the flexibility to work remotely the remainder of your time

 

• Collaborate with a multidisciplinary team of researchers and engineers to drive advancements in machine learning applications for organic material research.
• Participate in cutting-edge research by designing, developing, and validating innovative ML models tailored to molecule property prediction and generation.
• Develop and refine simulation platforms by integrating state-of-the-art tools and methodologies, ensuring robustness and scalability of research outputs.
• Integrate advanced data analytics frameworks to manage, preprocess, and interpret experimental data, enabling actionable insights for material innovation.
• Engage with academic communities toward paper publications.

What You Bring

• Pursuing Bachelors, Masters, or PhD in Computer Science, Computational Chemistry, Material Science preferred.
• Must have at least 1 academic quarter/semester remaining
• Experience in DFT,MD simulation tools such as ORCA, Q-Chem, GROMACS
• Experience in deep learning. Specific machine learning skills in GNN, reinforcement learning is plus.
• Experience in adapting standard machine learning methods (e.g. distributed clusters, and GPU clusters)
• A willingness to dive deep, experiment rapidly and get things done