Senior Software Engineer - Compute Infrastructure (Orchestration & Scheduling)

What you'll do

• Enhance Kubernetes cluster platforms for performance, scalability, and resilience; design unified scheduling for diverse workloads.
• Develop AI-model-driven scheduling to improve performance and resource utilization across heterogeneous resources and global data centers.
• Evolve compute platforms for ML and LLM training and inference, and write maintainable code informed by open-source and research advances.

What they ask for

• B.S./M.S. in Computer Science, Computer Engineering, or a related area; new graduates with a Ph.D. and strong publication records may be an exception.
• Solid understanding of at least one of: 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, or with resource efficiency and job scheduling.
• Experience with application scaling, workload co-location, isolation, or public cloud providers and their ML services.

Tools

Kubernetes, Docker, Python, Go, C++, Rust, Java, Ray

AI focus

Use AI models to optimize workload performance and resource utilization; build infrastructure for AI/ML and LLM training and inference workloads.