Search Engineering Intern: LLMs & Retrieval

TikTok

San Jose (CA)

On-site

USD 52,000 - 72,000

Full time

6 days ago
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Benefits offered by this job

Health insurance
Life insurance
Wellbeing benefits
Housing allowance

Job summary

TikTok Search is building the next-generation search experience for hundreds of millions of users. Our team develops TikTok’s search engine and explores combining traditional search with large language models to boost relevance and engagement.

We offer an internship program providing hands-on experience, industry exposure, and growth opportunities. Interns will work on challenging problems at the intersection of information retrieval, ML, NLP, and distributed systems, with the chance to advance

Qualifications

  • Currently pursuing a Bachelor's degree in Computer Science or a related technical discipline.
  • Solid foundation in data structures, algorithms, and operating systems.
  • Strong programming skills with one or more languages such as C++, Python, Go or Java.

Responsibilities

  • Build and optimize search systems for hundreds of millions of users.
  • Tackle large-scale problems in search quality, ranking, and retrieval.
  • Investigate integration of search technologies with LLMs to improve results.
  • Develop algorithms and systems for next-generation search experiences.
  • Collaborate with product managers and engineers to translate user needs into scalable solutions.
  • Apply ML, NLP, IR, and LLM techniques to enhance search quality.

Skills

C++
Python
Go
Java

Education

Bachelor's in CS

Job description

TikTok Search is building the next-generation search experience for hundreds of millions of users. Our team develops TikTok’s search engine and explores combining traditional search with large language models to boost relevance and engagement.

We offer an internship program providing hands-on experience, industry exposure, and growth opportunities. Interns will work on challenging problems at the intersection of information retrieval, ML, NLP, and distributed systems, with the chance to advance

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