Member of Technical Staff Cluster Infrastructure
RadixArk
Member of Technical Staff Cluster Infrastructure
RadixArk
Open-source AI infrastructure for large-scale inference
RadixArk is looking for a Member of Technical Staff Cluster Infrastructure to architect and scale the core compute platform that powers frontier-level AI training and inference.
You will design and operate highly reliable, high-performance GPU/TPU clusters, build next-generation scheduling and resource management systems, and push the limits of large-scale distributed infrastructure for AI workloads.
This role focuses on deep systems engineering across cluster architecture, networking, scheduling, and performance optimization. Your work will directly impact how efficiently frontier AI models are trained and served.
5+ years of experience in distributed systems, infrastructure, or large-scale compute platforms
Strong background in distributed systems design and systems architecture
Deep experience with cluster management systems (Kubernetes, Slurm, Ray, or custom schedulers)
Hands-on experience with GPU/TPU infrastructure in production environments
Strong Linux systems and networking fundamentals
Proficiency in Go, Rust, C++, or Python for production systems
Experience debugging complex multi-layer issues across hardware, OS, networking, and distributed services
Proven ability to design reliable, scalable systems in production
Strong Plus:
Experience with large-scale ML/AI workloads
Familiarity with RDMA, InfiniBand, or high-performance networking
Experience operating clusters at 1000+ GPU scale
Background in HPC or performance-critical systems
Open-source contributions in systems or infrastructure
Architect and scale large AI compute clusters for training and inference
Design cluster management, scheduling, and resource allocation systems
Optimize performance, utilization, and reliability of GPU/TPU clusters
Improve fault tolerance and system resilience at scale
Drive observability, monitoring, and performance profiling for cluster infrastructure
Collaborate with ML and systems engineers to support frontier AI workloads
Lead capacity planning and infrastructure scaling strategies
Build internal platforms and tooling to improve developer productivity
Document architecture, operational practices, and reliability strategies
Contribute to long-term platform vision and technical direction
RadixArk is an infrastructure-first company built by engineers who've shipped production AI systems, created SGLang (30K+ GitHub stars, the fastest open LLM serving engine), and developed Miles (our large-scale RL framework). Founded by AI infrastructure veterans from xAI and NVIDIA, 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.
Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
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.
RadixArk develops large-scale AI infrastructure for training and deploying models. Its core offering is the open-source SGLang engine that accelerates model inference, complemented by Miles, an open-source framework tailored for large-scale reinforcement learning and post-training tasks. The company provides managed infrastructure and production-grade tooling for model inference and post-training, with additional commercial products built on top of its open-source foundations. Revenue comes from managed hosting services and other paid offerings. Unlike many competitors, RadixArk pursues an open-core model to keep its core technology open and accessible to researchers, startups, and enterprises while monetizing via hosted services and premium tools. The goal is to democratize frontier AI infrastructure, reducing duplicated effort and enabling the AI community to train, deploy, and operate advanced models more efficiently and affordably.
Explore more opportunities at RadixArk