Research Scientist Intern - Large-Scale Machine Learning Systems (SysML) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

ByteDance
Singapore
Workplace: OnsiteInternshipFunction: Data Science & Machine LearningEducation: phdSkills: []

Join the Applied Machine Learning (AML) team to research and deploy next-generation large-scale multimodal recommendation systems. Work on algorithm-engineering co-design topics such as multimodal co-training, sparse MoE, memory networks, mixed precision, and end-to-end modeling with long sequences. You’ll build high-performance multimodal inference and training frameworks on PyTorch, explore heterogeneous hardware for multimodal recommendations, and improve recommendation accuracy while reducing training/inference bottlenecks.

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FursaFursa
ByteDance
ByteDance
1 month ago

Research Scientist Intern - Large-Scale Machine Learning Systems (SysML) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

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Last checked: 30 days agoStatus: Live
Reposted: similar role first listed 1 month ago

Job Summary

Join the Applied Machine Learning (AML) team to research and deploy next-generation large-scale multimodal recommendation systems. Work on algorithm-engineering co-design topics such as multimodal co-training, sparse MoE, memory networks, mixed precision, and end-to-end modeling with long sequences. You’ll build high-performance multimodal inference and training frameworks on PyTorch, explore heterogeneous hardware for multimodal recommendations, and improve recommendation accuracy while reducing training/inference bottlenecks.
Location: Singapore
Workplace: Onsite
Employment Type: Internship
Job Function: Data Science & Machine Learning
Seniority: Intern level

Key Responsibilities

  • •Research end-to-end ultra-large-scale multimodal recommendation systems to improve multimodal representation fusion and efficient fusion.
  • •Explore algorithm-engineering co-design approaches for multimodal co-training, including sparse MoE, memory networks, and mixed precision.
  • •Develop high-performance multimodal inference engines and training frameworks using the PyTorch framework.
  • •Investigate end-to-end modeling challenges involving stability, efficiency, and extended sequence lengths.
  • •Apply heterogeneous hardware adaptation to multimodal recommendation training and deployment to reduce computational costs.

Key Requirements

  • •Currently pursuing a PhD in Artificial Intelligence, Computer Science, Computer Engineering, or a related technical discipline.
  • •Proficiency in one or more programming languages such as C/C++/Go/Python/Java in a Linux environment.
  • •Familiarity with Kubernetes architecture and extensive experience in cloud-native system development.
  • •Experience with at least one mainstream machine learning framework (e.g., TensorFlow, PyTorch, MXNet).
  • •Backend development experience with Django, Flask, or related technologies.
Experience:Machine learningMultimodal recommendationCloud-nativeHigh-performance computingDistributed storage
Education:PhD / Doctorate in Artificial Intelligence, Computer Science, Computer Engineering (or related technical discipline)
Tech Stack:CC++GoPythonJavaLinuxKubernetesTensorFlowPyTorchMXNetDjangoFlaskMoEMixed precisionGPUAcceleratorsNetworkingDistributed storage

Company Brief

ByteDance
Develops consumer internet and content platforms, including TikTok and other apps for short-form video, news, and entertainment. It also builds advertising, commerce, and creator tools that connect audiences, brands, and publishers across global markets.
Industry: Digital Media
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Established Company
Headquarters: Beijing, China
Founded: 2012
WebsiteLinkedIn