Machine Learning Engineer Intern AML
Full-time
ByteDance
Responsibilities
- Design and build ML platform orchestration capabilities using Kubernetes Operators, container runtimes, and lifecycle management for jobs, services, and stateful workloads.
- Build multi-tenant resource and quota systems supporting priorities, preemption, fair sharing, elasticity, and cross-cluster scheduling.
- Improve GPU utilization and cost efficiency through resource pooling and FinOps.
- Build online model-serving lifecycle orchestration covering model and image distribution, deployment, upgrades, rollback, autoscaling, multi-cluster operation, and disaster recovery.
- Develop serving orchestration and traffic management for disaggregated serving clusters, including topology-aware scheduling, KV Cache affinity, intelligent request routing, and QoS/SLA management.
Requirements
- Currently pursuing 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 foundation in data structures, algorithms, and software engineering principles.
- Familiarity with Linux, operating systems, computer networks, concurrent programming, and distributed systems.
- Ability to investigate systems using source code, metrics, logs, profiling, and experiments.
- A systematic and quantitative approach to problem solving, including defining measurements, testing hypotheses, and validating improvements.
- Demonstrated ownership and collaboration through coursework, research, internships, open-source contributions, or other engineering projects.
- Preferred experience includes Kubernetes, container runtimes, resource scheduling, quota management, multi-tenant systems, or FinOps.
- Preferred qualifications include contributions to Kubernetes, Volcano, Koordinator, or OpenKruise infrastructure projects.
- Preferred experience includes vLLM, SGLang, Triton, KServe, Ray Serve, KV Cache, Continuous Batching, Prefill/Decode disaggregation, or model parallelism.
- Preferred experience includes online services, gateways, traffic management, autoscaling, performance optimization, highly available distributed systems, GPU/NPU programming, heterogeneous resource scheduling, model distribution, or inference performance analysis.
Benefits
- Internship providing hands-on experience, industry exposure, and opportunities to apply knowledge to real-world challenges.
- Participation in social events, learning programs, and development workshops alongside industry professionals.
- Applications are reviewed on a rolling basis, and candidates should state their availability and start and end dates on their resume.
Vacancy posted 24 days ago
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