ML Engineer Intern: Build Production-Scale Models

Coinbase

San Francisco (CA)

On-site

USD 68,000 - 97,000

Full time

5 days ago
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Job summary

Coinbase invites a Ph.D. candidate to join the Machine Learning team for a 12-week summer internship in 2027.

You will build, deploy, and operate ML models at production scale, tackling research-to-production projects that address security, personalization, and new crypto use cases. You will collaborate with senior engineers and product teams to identify practical ML applications for blockchain, present findings to stakeholders, and contribute to impactful, business-ready outputs.

Qualifications

  • Pursuing a Ph.D. with published or in-progress research in machine learning, deep learning, or a closely related field.
  • Demonstrated proficiency building and training models using ML frameworks such as PyTorch or TensorFlow.
  • Experience applying ML techniques to structured or unstructured datasets.

Responsibilities

  • Develop, deploy, and operate machine learning models and pipelines at production scale.
  • Drive an end-to-end research project applying modern ML techniques to solve a defined business problem.
  • Partner with senior engineers and product teams to identify new ML applications for blockchain and crypto use cases.
  • Present findings and recommendations to cross-functional stakeholders at the conclusion of your internship.

Skills

PhD research in ML
PyTorch
TensorFlow
Python
Production-quality code

Education

PhD candidate in ML

Tools

PyTorch
TensorFlow
Python

Job description

Coinbase invites a Ph.D. candidate to join the Machine Learning team for a 12-week summer internship in 2027.

You will build, deploy, and operate ML models at production scale, tackling research-to-production projects that address security, personalization, and new crypto use cases. You will collaborate with senior engineers and product teams to identify practical ML applications for blockchain, present findings to stakeholders, and contribute to impactful, business-ready outputs.

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