Member of Technical Staff — Inference-Core Engine

RadixArk•Palo Alto, CA

About The Position

RadixArk is seeking a Member of Technical Staff — Inference to advance large-scale AI inference. The role works on core systems serving frontier models across thousands of GPUs, optimizing performance, latency, throughput, and cost. It combines systems engineering, ML infrastructure, and performance optimization, with work that shapes how state-of-the-art models are deployed and experienced by users. This is a deeply technical, high-impact role for engineers who enjoy working near the hardware–software boundary and solving performance-critical problems at scale. RadixArk is an infrastructure-first company founded by AI infrastructure veterans from xAI and NVIDIA. The company builds open systems for inference and training, including SGLang and the Miles large-scale RL framework. Its team has optimized kernels serving billions of tokens daily, designed distributed training systems coordinating more than 10,000 GPUs, and contributed to infrastructure used by AI companies and research labs.

Requirements

At least 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, including model, runtime, kernel, and network.

Nice To Haves

Experience with LLM serving stacks such as SGLang, vLLM, or TensorRT-LLM.
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 (1,000+ GPUs).
Background in HPC or high-performance systems.

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 the reliability and scalability of inference infrastructure.
Build tooling for observability, profiling, and performance analysis.
Contribute to long-term inference architecture and strategy.