Member of Technical Staff — Inference-TPU

About the Role

RadixArk is looking for a Member of Technical Staff — TPU Systems to build high-performance inference and training systems using JAX, XLA, and Pallas. You'll push model workloads to their limits on TPU hardware, working on SGLang-JAX and other critical infrastructure that enables efficient deployment of frontier models on Google's tensor processing units.

Requirements

• 3+ years experience building production ML systems utilizing JAX/Torch, XLA, or TPU-focused frameworks.
• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or equivalent industry experience
• Deep understanding of XLA internals preferred: HLO, MLIR, operator fusion, SPMD partitioning, and sharding strategies.
• Strong performance tuning instincts across compiler and runtime layers
• Experience with distributed inference systems (e.g. SGLang, vLLM) or training frameworks (e.g. Miles, Alpa, Pathways)
• Proficiency in Python with demonstrated ability to write high-performance, production-quality code
• Experience writing custom GPU/TPU/AI Accelerator kernels. Familiarity with Pallas for kernel development is strongly preferred.

Responsibilities

• Build high-performance inference and training systems using JAX/XLA/Pallas, including SGLang-JAX
• Push large-model workloads to the limits on the newest TPU hardwares
• Optimize end-to-end latency and throughput for LLM serving on TPU infrastructure
• Design and implement SPMD strategies for efficient distributed inference and training
• Design and implement Pallas kernels for operations that require customized low level control for best performance
• Profile and optimize XLA compilation pipelines and HLO graph transformations
• Collaborate with kernel engineers and compiler teams to achieve performance wins across the stack
• Contribute to open-source projects with TPU optimization guides, benchmarks, and architectural insights