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# Student Researcher - (Seed Model - AI Foundation Models Infrastructure) - 2026 Start New

- **Company:** BYTEDANCE INC.
- **Location:** Seattle, WA, United States (Remote available)
- **Salary:** $108,680.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Profiling, Distributed Computing Environment, Python (Programming Language), Tensorflow, AI Infrastructure, Reinforcement Learning, Pytorch, Information Technology, Low Latency, Machine Learning Operations, Programming Languages
- **Published:** September 23, 2026
- **Apply:** https://www.gamesjobsdirect.com/job/bytedance/student-researcher-seed-model-ai-foundation-models-infrastructure-2026-start/359477

## About the Role

Minimum Qualifications: - Currently pursuing a Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related technical fields - Proficiency in at least one programming language such as Python or C++ - Familiarity with machine learning frameworks such as PyTorch or similar tools - Strong analytical and problem-solving skills - Ability to work collaboratively in a fast-paced technical environment - Interest in pursuing long-term work in ML systems or AI infrastructure

## Description

About the team The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models. As a project intern, you will have the opportunity to engage in impactful short-term projects that provide you with a glimpse of professional real-world experience. You will gain practical skills through on-the-job learning in a fast-paced work environment and develop a deeper understanding of your career interests. Applications will be reviewed on a rolling basis - we encourage you to apply early. Responsibilities - As an Infrastructure Intern, you may work on one or more of the following areas: - Assist in building and optimizing large-scale distributed training systems (e.g., data/model parallelism, memory efficiency, reliability) - Support the development and improvement of reinforcement learning training pipelines and post-training systems - Improve inference
performance, including latency, throughput, and system stability - Contribute to compiler or runtime optimizations for GPU and other accelerators - Conduct performance analysis, profiling, benchmarking, and bottleneck identification - Develop internal tools and automation to improve infrastructure efficiency and developer productivity - Collaborate with researchers and engineers to translate model requirements into scalable system solutions