Member of Technical Staff — Inference

Responsibilities

• Design and build large-scale inference systems for frontier AI models
• Optimize latency, throughput, and GPU utilization in production inference
• Develop and improve model serving architectures and runtimes
• Work on batching, scheduling, and memory management strategies
• Collaborate with kernel, compiler, and systems teams on performance optimization
• Debug performance bottlenecks across the stack
• Drive reliability and scalability of inference infrastructure
• Build tooling for observability, profiling, and performance analysis
• Contribute to long-term inference architecture and strategy

Requirements

• 5+ years of experience in systems engineering, ML infrastructure, or performance-critical backend systems
• Strong expertise in large-scale inference systems for LLMs or generative models
• Deep understanding of GPU architecture and performance characteristics
• Experience optimizing latency- and throughput-critical production systems
• Strong knowledge of distributed systems and networking fundamentals
• Proficiency in Python, Rust, C++, or Go for production systems
• Experience profiling and optimizing compute-intensive workloads
• Strong debugging skills across system layers (model, runtime, kernel, network)

Preferred Qualifications

• Experience with LLM serving stacks (SGLang, vLLM, TensorRT-LLM, etc.)
• Open-source contributions in ML or systems infrastructure
• Familiarity with CUDA, Triton, or custom kernel optimization
• Experience with batching, KV-cache management, and scheduling strategies
• Experience running inference at scale (1000+ GPUs)
• Background in HPC or high-performance systems

About the Company

RadixArk is an infrastructure-first company built by engineers who've shipped production AI systems, created SGLang, and developed Miles. We're on a mission to democratize frontier-level AI infrastructure by building world-class open systems for inference and training.