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Machine Learning Engineer Intern - GenAI - 2024 Summer (Phd)

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1mo ago

🚀 Summer Internship

San Jose

AI generated summary

  • You must be a PhD student in computer science or related field, graduating after Dec 2024, with strong coding skills, problem-solving abilities, and experience in machine learning algorithms and technologies.
  • You will develop advanced AI algorithms, improve natural language understanding, work on full-stack ML systems, and apply LLM techniques for large-scale AI applications.

Summer Internship

Data•San Jose


  • As an MLE Intern, you will be contributing to the development and optimization of our machine learning models. You will work closely with senior engineers and researchers to handle data processing, annotation analysis, and iterate on algorithm strategies to enhance our AI solutions. This role offers a unique opportunity to gain hands-on experience in the fast-paced field of AI and machine learning.


  • Currently enrolled in a PhD degree program in Computer Science, Engineering, Mathematics, or a related field with a focus on machine learning or artificial intelligence.
  • Graduating December 2024 onwards with the intent to return to degree program after the completion of the internship.
  • Able to commit to working for 12 weeks during Summer 2024.
  • Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.
  • Preferred Qualifications:
  • Strong design and coding capability, able to deliver high quality design and code.
  • Strong analytical and problem-solving skills, ability to see the essence of the problem from complicated data.
  • Proficient in one or more algorithmic strategies, such as NLU, Recall, or Sort. Familiarity with the latest Large Language Model (LLM) technologies.
  • Experience in algorithm development such as large-scale search, recommendation, LLM system.

Education requirements

Currently Studying

Area of Responsibilities



  • Develop and implement advanced algorithms for AI applications to drive exploration and application of cutting-edge technologies.
  • Improve natural language understanding, including intent recognition, structured/unstructured, characterization learning for short/long texts, etc.
  • Responsible for the full-stack development of large-scale machine learning systems and recommendation systems, exploring the personalized application of ultra-large-scale models.
  • Apply LLM-related techniques to explore information finding within large model-based AI applications.


Work type

Full time

Work mode



San Jose