RadixArk builds large-scale inference and training systems for the entire AI community, making frontier-level AI infrastructure open and accessible. Backed by Accel.
About the role
RadixArk is hiring a Member of Technical Staff — Performance in Palo Alto, CA — someone who can push LLM inference and training systems to the limit across real production workloads.
What they're looking for
- Strong systems engineering background, especially in performance-critical software
- Experience with GPU systems, distributed systems, inference serving, ML runtimes, or high-performance computing
- Familiarity with profiling tools, performance debugging, tracing, and benchmark methodology
- Comfort working with Python and C++
- Experience with CUDA, Triton, Pallas, ROCm, XLA, or kernel-level optimization is a strong plus
- Understanding of LLM inference concepts such as batching, KV cache, prefill/decode, speculative decoding, MoE, long context, and P99 latency
More about this role
RadixArk is hiring a Member of Technical Staff — Performance in Palo Alto, CA — someone who can push LLM inference and training systems to the limit across real production workloads.
You’ll work on the performance-critical path of SGLang, Miles, and the RadixArk infrastructure stack: latency, throughput, GPU utilization, memory efficiency, scheduling, batching, kernel behavior, distributed execution, and cost-per-token. This is not a generic benchmarking role. You’ll be working on the systems that determine whether frontier-scale AI workloads are actually usable, affordable, and reliable in production.
Our customers care about real numbers: P99 latency, TTFT, tokens/sec/GPU, throughput under long-context workloads, cost-per-million tokens, RL rollout efficiency, and training-inference consistency. You’ll help us measure, debug, and improve these systems across NVIDIA, AMD, Google TPU, and cloud partner environments.
This role is for someone who loves performance debugging, understands that small systems details can create massive product impact, and wants to work at the frontier of AI infrastructure.
- Analyze and improve performance across SGLang, Miles, and RadixArk production...
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