Computation (Machine Learning) Trainee

🎓 Internship

In-depth 8 weeks mentored program by senior engineers and researchers with an independent research project from computation group. Collaborate with peers and get hands on expertise.

LocationsBerkeley, CA • Shenzhen, China • Nanjing, China • Aliso Viejo, CA • Saint Louis, MO • Hybrid • RemoteGroupsComputationProgramsTraineeCreate Time2026-04-30

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Trainee

Start DateJune 15th or Rolling BasedEnd DateAugust 15th or Rolling BasedAttending TracksApplied Sciences Trainee • Computation (Quantum) Trainee • Computation (Machine Learning) TraineeLocation & ModeFull-time • Part-time • Hybrid • Shenzhen, China • Nanjing, China (Hybrid) • Aliso Viejo, CA • Saint Louis, MO • Remote • Berkeley, CA • On-siteGroupApplied Sciences • Computation

In-depth 8 / 12 weeks supervised independent or small group study with engineers, researchers, or faculty members. This program gives students opportunities to learn and work on real research problems and possibilities to continue to work with us as engineer / research intern level positions. This program is designed for students who have foundational preparation or strong interest in a specific direction, but are not yet experienced enough for advanced research or engineering topics.

Introduction

This trainee program includes listed directions: Deep Learning with C++, Machine Learning application to physical sciences, Realization of special optimizer, Realization of special neural network, and special modality data construction. Outstanding participants may be considered for further collaboration or paid roles based on performance and project needs. This position supports both mentor and free mode, note that this program is not designed for profit, all revenues are used to support lab expenses.

Trainee Mode

This program mainly provides Hybrid or remote opportunities. This program also provides onsite (Berkeley, CA) based on availability. Onsite seats is very limited for this program but requests will still be considered if there is any.

Free Format

The research topic will be given as the mentor format, but no mentor will be assigned to you. You can join regular meetings with the specified team based on their availability but they would not be responsible for your works. The qualification presentation would be processed within the specified team. For any research / project outputs, authorship will be determined based on actual contribution following standard academic practices. Intellectual property (IP) and any potential revenue distribution, if applicable, will be handled according to project-specific agreements and applicable regulations. No Charge

Mentor Format

This program is a paid mentorship-based research training format. You will work with 1–2 mentors on a selected research topic, typically in a small team (1–3 teammates). The program includes: – Up to five 1-hour mentorship sessions – Ongoing guidance and topic discussion as needed – Support in developing qualifications and refining the project The mentorship fee reflects the contracted time and effort of our mentors. For any research / project outputs, authorship will be determined based on actual contribution following standard academic practices. Intellectual property (IP) and any potential revenue distribution, if applicable, will be handled according to project-specific agreements and applicable regulations. Mentorship: $800 (Single Mentor); $1200 (Interdisciplinary Co-mentor)

Preferred Alignments

• Academic background in mathematics, Statistics, Data Science, Computer science, Physics, Chemistry, EE or related fields. • Familiarity with at least one of linear algebra, probability, numerical methods, or quantum mechanics. • Experience thinking about algorithm correctness, formal reasoning, or mathematically structured codebases. • Knows Python and some related machine learning application concepts. • Ability to read and reason about technical papers and translate them into implementable algorithms. • Curiosity about quantum system and machine learning.

Nice-to-have

• Experience with scientific computing or quantum simulation frameworks. • Background in compiler theory, symbolic computation, or automated reasoning. • Knows C++, PyTorch or JAX • Have experiences in High performance computing (CUDA, DLtraining, Physical Science Simulations)

Why Us

Computation group plays the core role of Forecaster AI, we provide support to theoretical natural science research platform, develop novel computational approaches for quantum science, artificial intelligence and are gradually applying to physics, chemistry, finance and so on. We initiated the philosophy of focus micro, FORECAST MACRO, and keep practicing public awareness, openness, and respect. We work rigorously, dream unlimited, and welcome peers to join. We can consider housing coverage and OPT/H1B sponsorship based on availability.