Adobe - San Jose, CA

posted 5 days ago

Full-time - Intern
San Jose, CA
Publishing Industries

About the position

The Machine Learning Engineer Intern position at Adobe is designed for students pursuing advanced degrees in Computer Science or related fields. The intern will apply AI and machine learning techniques to big-data problems, focusing on enhancing customer experiences through predictive models and optimization techniques. This role involves collaboration with product management and engineering teams to develop innovative products and features, particularly in the realm of large language models (LLMs).

Responsibilities

  • Develop and integrate LLM capabilities (GPT, Llama) into web-based cloud applications.
  • Perform prompt engineering and fine-tune LLMs, preparing datasets for fine-tuning.
  • Optimize for cost and latency when integrating LLM features.
  • 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 with an expected graduation date of December 2025 - June 2026.
  • Good understanding of machine learning or deep learning concepts.
  • Experience working with large language models like GPT, LLAMA, or Transformer-based architectures.
  • Proficient in one or more programming languages such as Python, TypeScript, or Java.
  • Outstanding command of data structures and algorithms.
  • Passion for staying up to date with the latest trends and technologies in AI/ML - in the cloud and on device.
  • Excellent communication and relationship skills, and a team player.

Nice-to-haves

  • Experience with multi-modal models.
  • Ability to participate in a full-time internship between May-September.

Benefits

  • Competitive hourly compensation ranging from $45.00 to $55.00 based on geographic market.
  • Exceptional work environment recognized globally.
  • Ongoing feedback and support through Adobe's unique Check-In approach.
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