Software Engineer - Compute Infrastructure (Orchestration & Scheduling)

What you'll do

• Enhance global Kubernetes cluster management for performance, scalability, and resilience.
• Design unified scheduling for diverse workloads and heterogeneous resources.
• Develop AI-assisted scheduling and compute platforms for efficient ML/LLM training and inference.

What they ask for

• B.S./M.S. in Computer Science, Computer Engineering, or a related area, with 2+ years of relevant industry experience; Ph.D. with strong publication records can be an exception.
• Solid understanding of at least one of: Unix/Linux environments, distributed and parallel systems, high-performance networking systems, or large-scale software systems.
• Experience designing, architecting, and building cloud or ML infrastructure, such as resource management, allocation, job scheduling, or monitoring.
• Familiarity with Docker and Kubernetes; proficiency in at least one of Python, Go, C++, Rust, or Java.

Nice to have

• Experience with large-scale cluster management systems such as Kubernetes, Ray, Yarn, or Mesos.
• Experience in resource-efficiency management, job scheduling, application scaling, workload co-location, or isolation; public-cloud and ML-service experience is also preferred.

Tools

Kubernetes, Docker, Ray, Yarn, Mesos, Python, Go, C++

AI focus

Use AI models to optimize workload performance and resource utilization, and build compute platforms for ML and LLM training and inference.