Software Engineer Graduate - AI Infrastructure, Compute Efficiency & Scheduling

We are the team that decides how ByteDance's massive fleet of computers gets used — turning hundreds of large-scale clusters and millions of daily workloads (from microservices to big data to the largest AI/LLM training and inference jobs) into a system that is highly efficient, reliable, and increasingly self-managing. At the heart of this is our own intelligent scheduling platform, Godel (open-sourced to the community), together with the systems that manage compute, memory, and power across our global data centers in North America, Europe, and Asia-Pacific. Our mission is simple to say and hard to do: squeeze the most value out of every unit of computing power, at a scale very few places in the world can offer, and increasingly let AI itself help run the infrastructure. As a new grad you'll work alongside senior engineers and global partners on problems whose solutions directly improve the performance and cost of every AI service we run — with real ownership and room to grow fast. Launch your career where inspiration is infinite. Successful candidates must be able to commit to an onboarding date by the end of 2027 — please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered in the order you apply (limit applies across ByteDance and its affiliates globally). Applications are reviewed on a rolling basis; we encourage you to apply early. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Candidates who pass resume screening will be invited to participate in Our Company's technical online assessment. Responsibilities You'll grow from scoped tasks toward end-to-end ownership on problems like: - Make AI infrastructure smarter. Help build the next generation of intelligent, AI-driven scheduling — deciding where and how millions of workloads run so the whole fleet is faster and more efficient. - Push efficiency to the frontier. Work on new ways to get more useful work out of the same hardware — smarter use of compute, memory, and power — directly lowering the cost of running AI at scale. - Operate at global scale, reliably. Help keep a massive, always-on platform stable across regions, and build automation (including AI-agent-assisted tools) that lets the system increasingly diagnose and heal itself. - Turn ideas into impact. Bring fresh thinking from your studies and projects into design discussions, prototype boldly, and see your work run in production for real users. Why This Role Matters The AI era runs on compute — and compute is scarce, expensive, and growing faster than the world can build it. How efficiently we use every chip, every server, and every watt is now one of the defining challenges for our company and the entire industry. Our team owns exactly that challenge at ByteDance's global scale: we make the infrastructure behind TikTok and our AI/LLM products faster, smarter, and dramatically more efficient — work that changes the economics of AI itself. Minimum Qualifications - Individuals who are completing or have recently completed a Bachelor’s/ Master’s degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline. - Proficiency in at least one major programming language such as Go, C++, Rust, Python, or Java. - Solid foundation in at least one of: operating systems, distributed systems, computer architecture, networking, or building large-scale software. Preferred Qualifications - Curiosity about large-scale systems, cloud infrastructure, or AI/ML infrastructure — shown through internships, coursework, research, competitions, or personal projects. - Any hands-on exposure to containers or cloud-native systems (e.g., Kubernetes) is a plus, but not required — we value strong fundamentals and a drive to learn. - Bonus: experience contributing to open-source systems projects.