Machine Learning System Scheduling Engineer Graduate (Applied Machine Learning) - 2027 Start

ByteDance develops technology products and platforms that inspire creativity and enrich life. The Machine Learning System Scheduling Engineer Graduate will design and develop resource scheduling systems for machine learning workloads, optimize heterogeneous compute, storage, and networking resources across data centers and clusters, and improve resource utilization for training and inference workloads under multi-tenant isolation.

What you'll do:

• Design and develop resource scheduling systems for machine learning workloads, supporting Volcano Ark and machine learning platform products
• Optimize orchestration and scheduling of heterogeneous compute resources, including GPUs, CPUs, and other accelerators, as well as storage resources such as cloud storage and networking resources such as VPC and RDMA, across multiple data centers and clusters
• Support scheduling requirements for offline training, online inference, and other workloads under strict multi-tenant isolation, improving overall resource utilization and efficiency

What they're looking for:

• Individuals who are completing or have recently completed a Bachelor's or Master's degree in Computer Science or a related discipline
• Proficient in one or two programming languages in a Linux environment, such as Go, Java, or Python
• Solid foundation in computer science and programming, familiarity with common algorithms and data structures, and good coding habits
• Familiar with at least one mainstream machine learning framework, such as TensorFlow, PyTorch, or an internally developed framework
• Familiar with Kubernetes architecture and ecosystem, as well as container technologies such as Docker, container, and Kata
• Understands distributed system principles and has participated in the design, development, or maintenance of large-scale distributed systems
• Practical experience in large-scale cluster online/offline resource scheduling; source-level understanding of one or more open-source schedulers such as Kubernetes, Volcano, YARN, or Mesos; familiarity with containerization and lightweight virtualization technologies
• Deep understanding and practical experience in scheduling topics such as multi-tenant quota governance, preemption, elasticity, fragmentation, tidal scheduling, co-location, and QoS; strong analytical and modeling ability for complex problems; GPU scheduling experience is preferred
• Experience in at least one of the following areas: CUDA, RDMA, AI infrastructure, hardware and software co-design, high-performance computing, machine learning hardware architecture such as GPUs, accelerators and networking, ML for systems, or distributed storage
• Hands-on experience in cloud-native machine learning systems is preferred

Benefits:

• Additional discretionary bonuses/incentives
• Restricted stock units
• Day one access to medical, dental, and vision insurance
• 401(k) savings plan with company match
• Paid parental leave
• Short-term and long-term disability coverage
• Life insurance
• Wellbeing benefits
• 10 paid holidays per year
• 10 paid sick days per year