Tech Lead Software Engineer - AI Compute Infrastructure

What you'll do

• Build high-performance, scalable, and resilient container-based cluster management and orchestration systems.
• Architect GPU and AI accelerator infrastructure for cost-efficient, secure ML platforms.
• Collaborate on LLM inference solutions and write production-ready code.

What they ask for

• B.S./M.S. in Computer Science, Computer Engineering, or a related field, with the stated experience requirement; a Ph.D. with strong systems/ML publications is also considered.
• Strong understanding of large model inference, distributed and parallel systems, and/or high-performance networking.
• Hands-on experience building cloud or ML infrastructure, such as resource management, scheduling, request routing, monitoring, or orchestration.
• Knowledge of Docker and Kubernetes, and proficiency in at least one of Go, Rust, Python, or C++.

Nice to have

• Experience contributing to or operating large-scale cluster management systems, or with GPU scheduling, orchestration, scaling, and isolation in production.
• GPU programming or inference-engine experience; familiarity with public cloud ML platforms or distributed ML systems.

Tools

AIBrix, Kubernetes, Ray, vLLM, SGLang, TensorRT-LLM, Docker, Go

AI focus

Builds GPU-optimized infrastructure and orchestration systems for ML workloads, especially large-scale LLM inference.