Senior Software Engineer - Compute Infrastructure (Orchestration & Scheduling)

What you'll do

• Enhance large-scale Kubernetes cluster platforms for performance, scalability, and resilience.
• Design and maintain unified scheduling for diverse workloads and resource pools.
• Develop AI-model-based 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; Ph.D. with strong publication records may be an exception.
• Solid understanding of at least one stated systems field, such as Unix/Linux, distributed and parallel systems, high-performance networking, or large-scale software systems.
• Experience designing, architecting, and building cloud or ML infrastructure, including resource management, allocation, job scheduling, or monitoring.
• Familiarity with Docker and Kubernetes, and 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 services experience is also noted.

Tools

Kubernetes, Docker, Ray, Yarn, Mesos, AWS, Azure, GCP

AI focus

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