Applied Machine Learning Engineer Intern – Large Language Models

Job Description

Software Engineer Intern (Applied Machine Learning-Enterprise) – 2026 Summer (BS/MS) | ByteDance

The Tone:
This is a Software Engineer Intern position at ByteDance, supporting the Applied Machine Learning Enterprise team. ByteDance, founded in 2012, is a global technology company known for products like TikTok, Lemon8, CapCut, Toutiao, Douyin, and Xigua, with a mission to inspire creativity and enrich life. This internship offers the opportunity to contribute to developing and operating massively distributed machine learning training and inference systems and services that support big model vendors and users worldwide. Interns will engage in real-world scenarios, building foundational skills and gaining industry exposure in large-scale system management, performance analysis, and hardware decision-making within the applied machine learning domain.

The TL;DR
• Role: Internship
• Type: Full-time (12 weeks)
• Location: Varies by selected city (e.g., Los Angeles)
• Pay: $42.75–$57 hourly
• Team: Applied Machine Learning Enterprise team combines system engineering and machine learning
• Mission: Develop and operate massively distributed machine learning training and inference systems and services globally, building a next-generation big model as a service platform.
• Tech Stack: PyTorch, TensorFlow, C++, Python, Go

What You’ll Actually Do
• Platform Development: Build a next-generation “big model as a service” platform designed to serve hundreds of large language model (LLM) based applications.
• Platform Maintenance: Develop and maintain the comprehensive “big model as a service” platform, covering offline training, finetuning, online inference, model management, and resource orchestration.
• Resource Management: Efficiently manage a huge number of GPU resources to ensure optimal computing power delivery for various applications.
• System Engineering: Build and maintain stable, reliable large-scale heterogeneous systems that integrate GPU, RDMA, and storage technologies.
• Strategic Input: Participate actively in the critical process of hardware and capacity decision-making for these distributed machine learning systems.

The Must-Haves
• Background: Currently pursuing a Bachelor’s or Master’s degree in Computer Science or a closely related technical field.
• Experience: Demonstrated experience in software development using at least one of the following programming languages: C++, Python, or Go. Practical experience in relevant business scenarios is also preferred.
• Skills: Proficient in deep learning frameworks such as PyTorch or TensorFlow; possesses strong software programming capabilities with a good code design and coding style; exhibits good teamwork and communication skills.
• Bonus: Experience in Natural Language Processing (NLP) and Large Language Model (LLM) technologies; experience or research in Alignment techniques for Large Language Models; experience in ModelOps, including large model management and finetuning workflow management; or contributions to top-tier conference papers such (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, KDD).