Graduate, LLM & Agent Search Systems

TIKTOK PTE. LTD.

Singapore

On-site

SGD 120,000 - 170,000

Full time

7 days ago
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Benefits offered by this job

Positive team atmosphere
Industry experts
Meals provided

Job summary

TikTok is seeking a PhD- level researcher to join the TT-Search Algorithm & Applied AI team in Singapore. You will work on building and optimizing the search-domain LLM foundation, deploying agents, and advancing multimodal retrieval and generation capabilities.

The role offers collaboration with industry experts and opportunities to push frontier AI in a fast-growing platform. Successful candidates will contribute to memory, evaluation, and scalable systems, with onboarding by year-end.

Qualifications

  • PhD in computer science or related discipline.
  • Strong ML/DL fundamentals and knowledge of LLM/Agent tech.
  • Experience with LLM pre-training, fine-tuning, alignment, or related projects.
  • Proficiency in PyTorch or TensorFlow for model training.
  • Familiarity with big data frameworks like MapReduce or Spark.

Responsibilities

  • Develop and optimize the search-domain LLM foundation.
  • Build Agent Harness and multi-agent orchestration.
  • Work on long-term memory and self-improvement loops.
  • Contribute to online evaluation and reward/verifier systems.
  • Collaborate on multimodal content generation and retrieval use cases.

Skills

PhD in computer science
ML/DL fundamentals
LLM/Agent projects
PyTorch or TensorFlow
Big data frameworks (MapReduce/Spark)

Education

PhD in Computer Science or related field

Tools

PyTorch
TensorFlow
Spark/Hadoop

Job description

TikTok is seeking a PhD- level researcher to join the TT-Search Algorithm & Applied AI team in Singapore. You will work on building and optimizing the search-domain LLM foundation, deploying agents, and advancing multimodal retrieval and generation capabilities.

The role offers collaboration with industry experts and opportunities to push frontier AI in a fast-growing platform. Successful candidates will contribute to memory, evaluation, and scalable systems, with onboarding by year-end.

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