Machine Learning Engineer Graduate (AML-Engine-Orchestration) - 2027 Start

The Data-AML-Engine Orchestration team builds large-scale machine learning infrastructure that powers online model serving across ByteDance products, including TikTok. We develop the orchestration, scheduling, and resource management systems that connect heterogeneous compute infrastructure with production ML workloads. You will work on systems that directly affect GPU utilization, serving latency and availability, infrastructure reliability, and MLE productivity. Depending on your background and interests, you may focus on one or more of the following areas., 1. Design and build foundational orchestration capabilities for machine learning platforms, including Kubernetes Operators, container runtimes, and lifecycle management for jobs, services, and stateful workloads.

1. Build multi-tenant resource and quota systems that support priorities, preemption, fair sharing, elasticity, and cross-cluster scheduling. Improve GPU utilization and cost efficiency through resource pooling and FinOps.
2. Build lifecycle orchestration for online model serving, including model and image distribution, deployment, upgrades, rollback, autoscaling, multi-cluster operation, and disaster recovery.
3. Build serving orchestration and traffic management capabilities for disaggregated serving clusters, including topology-aware scheduling, KV Cache affinity, intelligent request routing, and QoS/SLA management.Minimum Qualifications

Machine Learning, Resource Management, GPU (Graphics Processing Unit), Machine Learning, Web Infrastructure, Onboarding, Energy Efficiency, Disaster Recovery, Autoscaling, Topology, + Individuals who are completing or have recently completed a Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field.

• Proficiency in at least one of Go, C++, or Python, with a solid foundation in data structures, algorithms, and software engineering principles.
• Familiarity with Linux and a foundational understanding of operating systems, computer networks, concurrent programming, and distributed systems.
• Strong hands-on and exploratory abilities, with a willingness to investigate systems through source code, metrics, logs, profiling, and experiments.
• A systematic and quantitative approach to problem solving, with the ability to define measurements, test hypotheses, and validate system improvements.
• Demonstrated ownership and collaboration through coursework, research, internships, open-source contributions, or other engineering projects., o Experience with Kubernetes, container runtimes, resource scheduling, quota management, multi-tenant systems, or FinOps. o Contributions to open-source infrastructure projects such as Kubernetes, Volcano, Koordinator, or OpenKruise. o Experience with model serving systems such as vLLM, SGLang, Triton, KServe, or Ray Serve, or an understanding of KV Cache, Continuous Batching, Prefill/Decode disaggregation, or model parallelism. o Experience with online services, gateways, traffic management, autoscaling, performance optimization, or highly available distributed systems. o Experience with GPU/NPU programming, heterogeneous resource scheduling, model distribution, or inference performance analysis.