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

About Us

Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of products including TikTok, Lemon8, CapCut, and Pico, as well as platforms specific to the China market like Toutiao, Douyin, and Xigua, ByteDance makes it easier and more fun for people to connect with, consume, and create content.

ByteDance builds large-scale machine learning infrastructure that powers online model serving across its products. The Data-AML-Engine Orchestration team develops the orchestration, scheduling, and resource management systems connecting heterogeneous compute infrastructure with production ML workloads.

Responsibilities

• Design and build foundational orchestration capabilities for machine learning platforms, including Kubernetes Operators, container runtimes, and lifecycle management for jobs, services, and stateful workloads.
• 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.
• Build lifecycle orchestration for online model serving, including model and image distribution, deployment, upgrades, rollback, autoscaling, multi-cluster operation, and disaster recovery.
• 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.

Qualifications

Minimum Qualifications

• Completing or 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.

Preferred Qualifications

• Experience with Kubernetes, container runtimes, resource scheduling, quota management, multi-tenant systems, or FinOps.
• Contributions to open-source infrastructure projects such as Kubernetes, Volcano, Koordinator, or OpenKruise.
• 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.
• Experience with online services, gateways, traffic management, autoscaling, performance optimization, or highly available distributed systems.
• Experience with GPU/NPU programming, heterogeneous resource scheduling, model distribution, or inference performance analysis.

Pay

The base salary range for this position is $128,000 - $256,000 annually. Compensation may vary based on qualifications, skills, competencies, experience, and location. Base pay is one part of the total package, which may include additional discretionary bonuses, incentives, and restricted stock units.

Benefits

• 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, and life insurance.
• Wellbeing benefits.
• 10 paid holidays per year.
• 10 paid sick days per year.
• 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).