Machine Learning Intern, AI and Automation

Sorry, this job was removed at 06:20 p.m. (CST) on Monday, Apr 21, 2025
3 Locations
Remote
Big Data • Cloud • Software • Analytics
The Role

This is a remote position that can be based anywhere in the United States or Canada.
Calix is looking for a Machine Learning intern with a strong foundation in ML fundamentals and algorithms to join our Products Team for the Summer. In this role, you will be part of a unique and award-winning internship program within the company. The program provides the opportunity to learn new skills through training and on the job learning.
In this role, you will work on developing and implementing sophisticated machine learning solutions, including both traditional ML models and cutting-edge generative AI applications. You'll have the opportunity to work with large-scale datasets and contribute to the entire ML pipeline, from data preprocessing to model deployment.

Responsibilities and Duties: 

  • Design and implement machine learning algorithms for various applications, including classification, regression, and clustering problems
  • Conduct thorough model evaluation, validation, and performance optimization using industry-standard metrics
  • Implement and fine-tune various ML models using frameworks such as TensorFlow, PyTorch, and Scikit-learn
  • Assist in the development of generative AI applications using state-of-the-art models
  • Document model architectures, experimental results, and technical specifications

Required Qualifications: 

  • Currently pursuing a Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related technical field. Preference will be given to students with prior work experience 
  • Strong foundation in mathematics and statistics, including:
    • Linear algebra and calculus
    • Probability theory and statistical modeling
    • Optimization techniques
  • Proficiency in Python programming with experience in ML libraries and frameworks
  • Understanding of fundamental machine learning concepts:
    • Supervised and unsupervised learning algorithms
    • Model evaluation and validation techniques
    • Feature engineering and selection methods
  • Experience with version control systems (Git) and data processing tools
  • Experience with Unix-based OS
  • Able to work for the complete summer break (May - August or June - September)

Preferred Qualifications: 

  • Previous coursework or projects in machine learning, deep learning, or AI
  • Experience with big data technologies such as Apache Spark
  • Familiarity with cloud platforms (AWS, Google Cloud) and their ML services
  • Knowledge of MLOps practices and model deployment workflows
  • Understanding of deep learning architectures and their applications
  • Experience with Natural Language Processing (NLP) concepts and techniques

 Location: 

  • Remote-based position located in the United States or Canada. 

#LI-Remote

Compensation will vary based on geographical location (see below) within the United States. Individual pay is determined by the candidate's location of residence and multiple factors, including job-related skills, experience, and education.

For more information on our benefits click here.

There are different ranges applied to specific locations. The average base pay range (or OTE range for sales) in the U.S. for the position is listed below.

San Francisco Bay Area Only:

27.60 - 34.50 USD Hourly

All Other Locations:

24.00 - 30.00 USD Hourly

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The Company
HQ: San Jose, CA
1,618 Employees
Year Founded: 1999

What We Do

Innovative communications service providers rely on Calix platforms to help them master and monetize the complex infrastructure between their subscribers and the cloud. Calix is the leading global provider of the cloud and software platforms, systems, and services required to deliver the unified access network and smart premises of tomorrow. Our platforms and services help our customers build next generation networks by embracing a DevOps operating model, optimize the subscriber experience by leveraging big data analytics, and turn the complexity of the smart home and business into new revenue streams.

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