Machine Learning Engineer Intern

Coinbase is hiring a Machine Learning Engineer Intern in San Francisco, United States. It pays $60/hr and Level rates it ; you can apply on Coinbase.

Level's read on this job

Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
The posting allows some remote work.
Tools and skills the posting cites include PyTorch, TensorFlow and Python.
Level classifies it as an internship.
Level lists 226 other open roles at Coinbase.

AI in this role

Machine learning intern building production-scale ML models and pipelines for crypto use cases at Coinbase.

pytorchtensorflowpython

machine-learningdeep-learningsupervised-learningreinforcement-learning

Ready to do the most impactful work of your career? At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase.

This is a 12-week internship during summer 2027.

You'll join Coinbase's Machine Learning team and work alongside senior engineers building ML models and pipelines that make our platform more secure, personalize user experiences, and unlock new use cases for crypto. As an MLE intern, you'll take ownership of a research-to-production project, applying cutting-edge ML techniques to real-world problems at blockchain scale.

What you'll do:

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

Required Skills and Experience:

Currently 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 including supervised learning, unsupervised learning, or reinforcement learning to structured or unstructured datasets
Familiarity with software engineering fundamentals including version control, testing, and writing production-quality Python code
Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Req ID: P78143

#LI-Hybrid

Pay Transparency Notice: Depending on your work location, the target hourly rate for this position can range as detailed below. Hourly Rate:$60—$60 USD

Application Limit: Candidates may submit a maximum of 3 applications within a 6-month period.
Equal Opportunity Employer: Coinbase is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or genetic information. Applicants with criminal histories will be considered consistent with applicable federal, state, and local laws.
US Applicants: View Employee Rights, Know Your Rights, and E-Verify Notice of Participation.
Accommodations: If you are an individual with a disability who needs a reasonable accommodation, email us your request and contact info at accommodations[at]coinbase.com. Need screen reading technology? Click here to download a free compatible screen reader and view the tutorial.
Data Privacy & Arbitration: By submitting your application, you agree to our Candidate Privacy Notice. US applicants: By submitting your application, you agree to Arbitration of Disputes.

How we rate this

Machine Learning Engineer Intern at Coinbase rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Machine Learning Engineer Intern is an open job at Coinbase.
Level rates this role AI Level 4 of 4: Builds AI.
The listed pay is $60/hr.
The role is based in Hybrid - San Francisco, CA and is marked remote.
The job was posted on September 8, 2026.
Seniority: intern.
Applications are handled by Coinbase. Apply on Coinbase

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Machine learningDeep learningSupervised LearningReinforcement learningPyTorchTensorFlowPython

Also common in machine learning engineer jobs at this level

LLM AWS C++Kubernetes Spark Fine-tuning

Pay

Typical machine learning engineer job: $199k

Use the typical machine learning engineer figure as a reference when pay comes up. See machine learning engineer jobs

Questions you could be asked

Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where deep learning was part of your work. What did you do?
Tell me about a project where supervised learning was part of your work. What did you do?
Tell me about a project where reinforcement learning was part of your work. What did you do?
Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Machine learning, Deep learning, Supervised Learning, Reinforcement learning, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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