Performance Engineer

RadixArk•Palo Alto, CA

8d•Onsite

About The Position

RadixArk is hiring a Performance Engineer 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.

Requirements

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++
Ability to debug messy real-world performance issues across software, hardware, and infrastructure layers
Strong communication skills — you should be able to explain performance tradeoffs to both engineers and customers

Nice To Haves

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
Prior experience with production AI infrastructure, cloud GPU environments, or open-source ML systems is a plus

Responsibilities

Analyze and improve performance across SGLang, Miles, and RadixArk production deployments
Benchmark LLM inference and training workloads across GPUs, TPUs, and cloud environments
Optimize latency, throughput, memory usage, batching, scheduling, routing, and GPU utilization
Investigate performance regressions in real customer environments
Work closely with kernel, runtime, distributed systems, and product engineers
Build internal tooling for profiling, tracing, benchmarking, and regression detection
Translate customer workload characteristics into concrete performance tuning strategies
Help define performance metrics that matter commercially, including cost-per-token and serving efficiency
Partner with customers and cloud partners on deep technical evaluations
Contribute performance insights back to open-source SGLang and Miles