Member of Technical Staff — Cluster Infrastructure & Supercomputing
RadixArk
Salary undisclosed
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Description
About the Role
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.
Requirements
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
Responsibilities
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
About RadixArk
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.
Compensation
Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
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.
About this role
RadixArk is building infrastructure for frontier AI at scale, and this role sits at the center of that effort. You'd architect and operate GPU/TPU clusters that train and serve large-scale AI models, designing the scheduling systems, resource management layers, and performance optimization strategies that determine how efficiently these workloads run. The work spans cluster architecture, networking, distributed systems design, and deep debugging across hardware, OS, and application layers—this is systems engineering at the infrastructure level, not application development.
The ideal candidate brings 5+ years working on distributed systems or large-scale compute platforms, with hands-on production experience managing clusters (Kubernetes, Slurm, Ray, or custom schedulers) and strong fundamentals in Linux, networking, and systems programming. You'd need to be comfortable operating at scale—ideally you've worked with GPU infrastructure in production, debugged complex multi-layer failures, and designed systems that are both reliable and performant. Experience with ML workloads, high-performance networking (RDMA, InfiniBand), or HPC is valuable but not required; what matters most is a track record shipping production systems.
Beyond the technical bar, RadixArk is looking for someone who can lead—you'll shape capacity planning, drive observability and monitoring strategies, collaborate across teams, and contribute to the long-term platform vision. The role is in-office in Palo Alto and offers $200K–$400K salary plus equity, depending on background.
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Pay for this role
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