Foundation Models Intern: ML Research & Fine-Tuning

42dot; San Francisco (CA); On-site; USD 34,000 - 55,000; Full time

Job summary

42dot, a mobility AI company and Hyundai Motor Group's Global Software Center, is seeking an intern to help develop and evaluate foundation models. You will work with ML engineers, train and fine-tune models, and own a focused project with clear deliverables.

You will reproduce baseline results, improve fine-tuning and data quality, run experiments, and contribute reusable code and documentation. Strong Python and PyTorch experience, ML fundamentals, and willingness to learn are essential.

Qualifications

• Strong Python programming skills and familiarity with PyTorch or a similar framework.
• Understanding of machine learning fundamentals, deep learning, and transformer architectures.
• Experience with an ML project through coursework, research, or personal work.
• Ability to debug problems, interpret experimental results, and communicate clearly.
• Curiosity, initiative, and willingness to learn.

Responsibilities

• Reproduce baseline results and understand our models, datasets, and evaluation workflows.
• Implement and evaluate improvements in fine-tuning, data quality, or inference efficiency.
• Run controlled experiments and analyze model performance, computational cost, and failure cases.
• Contribute reusable code, evaluation tools, and technical documentation.
• Present your findings and recommendations to the team.

Skills

Python programming

PyTorch

ML fundamentals

Experimentation

Communication

Tools

Hugging Face Transformers

GPUs

Linux

Git

Experiment tracking tools

Job description

42dot, a mobility AI company and Hyundai Motor Group's Global Software Center, is seeking an intern to help develop and evaluate foundation models. You will work with ML engineers, train and fine-tune models, and own a focused project with clear deliverables.

You will reproduce baseline results, improve fine-tuning and data quality, run experiments, and contribute reusable code and documentation. Strong Python and PyTorch experience, ML fundamentals, and willingness to learn are essential.