Software Engineer, Language Models; LLM – Language Models

Software Engineer Intern (Applied Machine Learning-Enterprise) – 2026 Start (PhD) | Byte Dance

This is an internship at Byte Dance, for a 2026 start, specifically for PhD students. Byte Dance builds a suite of over a dozen products, including Tik Tok, Lemon8, Cap Cut, Toutiao, Douyin, and Xigua, with a mission to inspire creativity and enrich life. This role contributes to the Applied Machine Learning Enterprise team by helping develop and operate a big model service platform that offers Model-as-a-Service solutions, making a direct impact on the organization’s future plans and emerging technologies.

• Role:
  Internship
• Type:
  Temporary
• Location:
  
  Not Specified
• Pay: $60 hourly
• Team:
  Applied Machine Learning Enterprise team
• Mission:
  Develop and operate a big model service platform that offers Model-as-a-Service solutions for businesses.
• Tech Stack:
  PyTorch, Tensor Flow, C++, Python, Go

• Platform Building:
  Build a next‑generation big model as a service platform to serve hundreds of LLM-based applications.
• System Operations:
  Develop and maintain the big model as a service platform, including offline training/fine tuning, online inference, and model management.
• Resource Optimization:
  Manage a huge number of GPU resources and provide computing power efficiently.
• Strategic Contribution:
  Actively contribute to Byte Dance’s products, research, and emerging technologies.
• Solution Delivery:
  Offer Model-as-a-Service solutions to both big model vendors and users.

• Background:
  Currently pursuing a PhD in Computer Science or a related technical field.
• Experience:
  
  Software development in at least one of C++, Python, or Go, demonstrating strong programming capabilities with good code design and style.
• Skills:
  
  Proficient in deep learning frameworks such as PyTorch or Tensor Flow, along with good teamwork and communication skills.
• Bonus:
  Experience in NLP and LLM technologies, research or experience in Alignment for Large Language Models, experience in Model Ops, or contributions to top‑tier conference papers.