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 seeking a Member of Technical Staff, Developer Technology (DevTech) to make LLM inference and training dramatically faster, cheaper, and more accessible on modern GPU hardware. Our systems sit at the center of how modern AI is served and trained: SGLang is a high-performance inference engine that serves trillions of tokens daily across leading AI companies and research labs, and Miles is our reinforcement-learning post-training framework for large-scale LLM and MoE models.
What they're looking for
- Minimum Requirements
- 4+ years of experience in GPU systems, LLM infrastructure, or performance engineering
- Strong profiling and debugging skills: able to root-cause performance and correctness issues across the stack
- Hands-on GPU programming experience in at least one of CUDA, ROCm, or Triton, and willingness to work across platforms
- Strong programming skills in Python plus C++ or CUDA
- Comfortable making progress on hard, ambiguous problems with little context to start from, and fast to ramp into unfamiliar systems, codebases, and domains
More about this role
RadixArk is seeking a Member of Technical Staff, Developer Technology (DevTech) to make LLM inference and training dramatically faster, cheaper, and more accessible on modern GPU hardware. Our systems sit at the center of how modern AI is served and trained: SGLang is a high-performance inference engine that serves trillions of tokens daily across leading AI companies and research labs, and Miles is our reinforcement-learning post-training framework for large-scale LLM and MoE models. Your work directly advances our mission to democratize AI: every improvement you ship lowers the cost and raises the ceiling of what developers everywhere can build.
As our technical face to a community of expert users and partners, you'll push the performance of SGLang and Miles through the lens of real production workloads. You'll profile and optimize GPU performance, enable new models and hardware, build kernels, deliver day-0 model support, and push the limits of inference and training. Working in close partnership with leading teams across the ecosystem, you'll turn their hardest, most ambiguous problems into concrete wins and clear guidance, and feed those improvements back into our systems and...
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