Intern, Computational Chemistry Machine Learning Engineer

What You’ll Learn

Project: Perform predictive modeling of material properties using a machine leaming approach with limited data, aiming to attain a high level of model accuracy and fidelity.

• Skills You’ll Learn:
  • Strong understanding of molecular properties
  • Strong understanding of machine learning with especial focus on GNN, RL
  • Strong data handling and process for chemistry data

What You’ll Do

• Utilize GNN and RL Frameworks
• Design and Implement GNN and RL Models
• Conduct Regular Experiments and analysis
• Complete other responsibilities as assigned.

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

What You Bring

• Pursuing a Ph.D. in Computer Engineering or related field
• Strong understanding of machine learning concepts and techniques, particularly in GNN and RL.
• Proficiency in Python programming and experience with relevant libraries.
• Familiarity with GNN frameworks (e.g., PyTorch Geometric, DGL) and RL environments (e.g., OpenAl Gym). Data preprocessing skills and the ability to work with structured and graph data.
• An avid learner, you approach challenges with curiosity and resilience, seeking data to help build understanding.
• You’re collaborative, building relationships, humbly offering support and openly welcoming approaches.
• Innovative and creative, you proactively explore new ideas and adapt quickly to change.