Machine Learning Research Intern

🎓 Internship

Prototype and evaluate learning-based approaches for scientific modeling and optimization.

LocationsBerkeley, CA / RemoteGroupsComputationProgramsEngineer / Trainee / Summer Onsite ProgramCreate Time2026-04-27
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Summer Intern

Start DateJune 5th or June 16thEnd DateAugust 5th or September 5thAttending TracksMath Engineer (Quantum) • Math Engineer (AI / ML) • Applied Science Engineer (Chemistry) • Applied Science Engineer (System) • Machine Learning Research InternLocation & ModeBerkeley, CA • Berkeley, CA (Hybrid) • Remote • Nanjing, ChinaGroupApplied Sciences • Computation

This Summer Internship Program invites students to collaborate on cutting-edge computational modeling of microscopic physical systems and intelligent understanding of the physical world, working alongside researchers and engineers across disciplines. Participants engage in a flexible, research-driven environment with opportunities to contribute to meaningful projects and gain exposure to diverse perspectives, with potential stipends or continued collaboration based on contributions and funding availability.

Introduction

We are looking for a Machine Learning Engineer Intern to support the development of computational methods for next-generation microscopic modeling systems.

What we are looking for

Background in Computer Science, Mathematics, Physics, or related fields Familiarity with machine learning fundamentals (neural networks, optimization, etc.) Basic programming skills (Python required) Interest in mathematically grounded or scientific ML systems Comfortable reading and implementing technical ideas

Preferred Alignments

Junior or Senior knows Machine Learning advancements.

Nice to Have

Experience with PyTorch, JAX, or similar frameworks Exposure to scientific computing or structured models Interest in quantum systems, optimization, or learning theory

What You Will Be Doing

Implement and experiment with machine learning models Assist in developing computational pipelines and tools Work with researchers to translate ideas into code Run experiments and analyze results Support research in areas such as: learning systems optimization quantum or structured models

Technical Participation

Role-Specific Responsibilities

Role Positioning

This role sits within the computation group, focusing on building and testing machine learning components that support research in microscopic modeling and intelligent systems.

Why us

We believe in win-win growth and interdisciplinary communications. You can either research in a certain area in a stable professional environment, or go around freely, from research platform infrastructure, industry analysis, fiscal policies to future world imaginations.