Member of Technical Staff — Inference
About the Role
RadixArk is seeking a
Member of Technical Staff — Inference
to push the limits of large-scale AI inference.
You will work on the core systems that serve frontier models at scale, optimizing performance, latency, throughput, and cost across thousands of GPUs. This role sits at the intersection of systems engineering, ML infrastructure, and performance optimization.
Your work will directly shape how state-of-the-art models are deployed and experienced by users worldwide.
This is a deeply technical, high-impact role for engineers who enjoy working close to the hardware–software boundary and solving performance-critical problems at scale.
Requirements
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 C++, Rust, Go, or Python for production systems
Experience profiling and optimizing compute-intensive workloads
Strong debugging skills across system layers (model, runtime, kernel, network)
Strong Plus
Experience with LLM serving stacks (vLLM, TensorRT-LLM, SGLang, etc.)
Familiarity with CUDA, Triton, or custom kernel optimization
Experience with batching, KV-cache management, and scheduling strategies
Experience running inference at scale (1000+ GPUs)
Background in HPC or high-performance systems
Open-source contributions in ML or systems infrastructure
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 reliability and scalability of inference infrastructure
Build tooling for observability, profiling, and performance analysis
Contribute to long-term inference architecture and strategy
About RadixArk
RadixArk is an infrastructure-first company built by engineers who've shipped production AI systems, created SGLang (20K+ GitHub stars, the fastest open LLM serving engine), and developed Miles (our large-scale RL framework).
We're on a mission to democratize frontier-level AI infrastructure by building world-class open systems for inference and training.
Our team has optimized kernels serving billions of tokens daily, designed distributed training systems coordinating 10,000+ GPUs, and contributed to infrastructure that powers leading AI companies and research labs.
We're backed by well-known infrastructure investors and partner with Nvidia, Google, AWS, and frontier AI labs.
Join us in building infrastructure that gives real leverage back to the AI community.
Compensation
We offer competitive compensation with meaningful equity, comprehensive benefits, and flexible work arrangements. Compensation depends on location, experience, and level.
Equal Opportunity
RadixArk is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.
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 C++, Rust, Go, or Python for production systems
Experience profiling and optimizing compute-intensive workloads
Strong debugging skills across system layers (model, runtime, kernel, network)
Strong Plus
Experience with LLM serving stacks (vLLM, TensorRT-LLM, SGLang, etc.)
Familiarity with CUDA, Triton, or custom kernel optimization
Experience with batching, KV-cache management, and scheduling strategies
Experience running inference at scale (1000+ GPUs)
Background in HPC or high-performance systems
Open-source contributions in ML or systems infrastructure
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 reliability and scalability of inference infrastructure
Build tooling for observability, profiling, and performance analysis
Contribute to long-term inference architecture and strategy
About RadixArk
RadixArk is an infrastructure-first company built by engineers who've shipped production AI systems, created SGLang (20K+ GitHub stars, the fastest open LLM serving engine), and developed Miles (our large-scale RL framework).
We're on a mission to democratize frontier-level AI infrastructure by building world-class open systems for inference and training.
Our team has optimized kernels serving billions of tokens daily, designed distributed training systems coordinating 10,000+ GPUs, and contributed to infrastructure that powers leading AI companies and research labs.
We're backed by well-known infrastructure investors and partner with Nvidia, Google, AWS, and frontier AI labs.
Join us in building infrastructure that gives real leverage back to the AI community.
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