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Wasabi Technologies

Co-op – AI / Machine Learning Engineering

Sorry, this job was removed at 06:10 p.m. (PST) on Thursday, Apr 30, 2026
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Remote
Hiring Remotely in United States of America
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Remote
Hiring Remotely in United States of America

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At Wasabi, we’re a proven collection of pioneers, visionaries and disruptive doers. We see things differently than our competitors, and we make our mark in the industry by challenging the norm and delivering the unexpected and improbable. We’re a fast-growing company taking the Cloud Storage industry by storm and recognized as one of the best places to work in Boston. 

Wasabi hot cloud storage is a new class and category of cloud storage, breaking all traditional barriers and boundaries of storage with a disruptive value proposition of being 1/5th the cost of AWS S3, faster than the competition, with no fees for egress or API requests. Cloud storage has never been so simple, so fast and so inexpensive. It’s all part of our vision to make cloud storage the next great global utility, just like electricity.

Role Description: 2026 Summer and Fall Co-op – AI / Machine Learning Engineering. 

Role Purpose: 

Wasabi Technologies is seeking a motivated AI/ML Co-op student to join our Engineering team for the summer and fall 2026 term. This opportunity is ideal for students pursuing a degree in Computer Science, or Machine Learning. 

In this role, you will gain hands-on experience building and supporting machine learning pipelines and AI services that power real business applications. You will work closely with senior engineers and data scientists to help deploy, monitor, and improve production ML systems in a cloud-first environment. 

Please note: Students must currently reside in the United States to be eligible for this position.  

Key Responsibilities: 

  • Assist in building and maintaining ML pipelines, including data preparation, feature engineering, model training, and deployment. 
  • Support model packaging and API integration using tools such as Python, Docker, and ML frameworks (PyTorch, TensorFlow, or scikit-learn).
  • Help monitor model performance, latency, and data drift, and contribute to testing and validation workflows. 
  • Participate in CI/CD processes, code review, and documentation to ensure reliable and maintainable AI services. 
  • Collaborate with engineers, data scientists, and product teams to translate business. requirements into scalable ML solutions. 

Program Requirements: 

  • Currently pursuing a Master’s or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field. 
  • Working knowledge of Python; exposure to C++, Rust, or Go is a plus but not required. 
  • Familiarity with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn through coursework, research, or personal projects. 
  • Exposure to tools like Git, Docker, or cloud platforms (AWS, GCP, or Azure) is helpful but not required. 
  • Interest in learning how to deploy and monitor ML models in real-world environments. 
  • Strong problem-solving skills and the ability to communicate and collaborate in a team setting.
  • The co-op must be enrolled in classes in the United States and must live in the United States. 

Base Salary – $32 per hour. 

The base salary range reflects the full range for this position at Wasabi Technologies. At Wasabi, we believe in paying fairly and competitively, and individual compensation is determined based on factors such as job-related experience, skills, location, and internal equity. Base pay is just one part of our total rewards approach. Depending on role eligibility, Wasabi team members may also receive additional compensation and benefits, including bonus or variable compensation, equity, and a comprehensive benefits package designed to support both professional growth and personal well-being. 

Wasabi Technologies is an Equal Opportunity Employer. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.

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