Machine Learning Intern

4 weeks ago


San Jose, California, United States Adobe Full time
Job Description

We are seeking a highly motivated and talented Machine Learning Intern to join our team at Adobe. As a Machine Learning Intern, you will have the opportunity to work on exciting projects that apply AI and machine learning techniques to big-data problems to help Adobe better understand, lead, and optimize the experience of its customers.

Key Responsibilities:

  • Develop predictive models on large-scale datasets to address various business problems with statistical modeling, machine learning, and analytics techniques.
  • Develop and implement scalable, efficient, and interpretable modeling algorithms that can work with large-scale data in production systems.
  • Collaborate with product management and engineering groups to develop new products and features.

Requirements:

  • Currently enrolled full-time and pursuing a Master's or PhD degree in Computer Science, Computer Engineering, or equivalent experience required with an expected graduation date of December 2026.
  • Good understanding of statistical modeling, machine learning, deep learning, or data analytics concepts.
  • Familiar with distributed frameworks, HPC communication libraries, advanced parallelism techniques for large-scale training.
  • Proficient in one or more programming languages such as Python, Java, and C.
  • Familiar with one or more machine learning or statistical modeling tools such as R, Matlab, and scikit-learn.
  • Strong analytical and quantitative problem-solving ability.
  • Excellent communication, relationship skills, and a team player.

What We Offer:

  • A competitive salary range of $61.00 hourly.
  • A dynamic and inclusive work environment.
  • Opportunities for professional growth and development.

Adobe is an Equal Employment Opportunity and affirmative action employer. We do not discriminate based on gender, race, or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other applicable characteristics protected by law.

Learn more about our commitment to diversity and inclusion.



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