Responsibilities
- Assist in building and optimizing large-scale distributed training systems, including data/model parallelism, memory efficiency, and reliability.
- Support reinforcement learning training pipelines and post-training systems.
- Improve inference performance, including latency, throughput, and system stability.
- Contribute to compiler or runtime optimizations for GPUs 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.
Requirements
- Currently pursuing a bachelor's or master's degree in computer science, electrical engineering, or a related technical field.
- 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.
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About ByteDance
ByteDance is a global incubator of platforms at the cutting edge of commerce, content, entertainment and enterprise services - over 2.5bn people interact with ByteDance products including TikTok. Creation is the core of ByteDance's purpose. Our products are built to help imaginations thrive. This is doubly true of the teams that make our innovations possible. Together, we inspire creativity and enrich life - a mission we aim towards achieving every day. At ByteDance, we create together and grow together. That's how we drive impact - for ourselves, our company, and the users we serve. We are committed to building a safe, healthy and positive online environment for all our users. We have over 110,000 employees based in more than 30 countries globally. Join us.