Member of Technical Staff — Inference-Core Engine

RadixArk; Palo Alto (CA); In loco; USD 200.000 - 400.000; Tempo pieno

Descrizione del lavoro

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 is a deeply technical role at the hardware-software boundary. The compensation for this role ranges from $200,000 to $400,000 USD per year plus equity.

Competenze

• 5+ years in systems engineering, ML infrastructure, or performance-critical backends.
• Proven expertise in large-scale inference for LLMs or generative models.
• Deep understanding of GPU architecture and performance.
• Proficient in Python, Rust, C++, or Go for production systems.
• Experience profiling and optimizing compute-intensive workloads.
• Strong debugging across model, runtime, kernel, and network layers.

Mansioni

• 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.

Conoscenze

Large-scale inference systems

GPU architecture

Distributed systems

Latency optimization

Profiling

Debugging

Performance optimization

Python

Rust

C++

Go

Strumenti

Python

Rust

C++

Go

SGLang

vLLM

TensorRT-LLM

CUDA

Triton

Descrizione del lavoro

About the Role

RadixArk is seeking aMember of Technical Staff — Inferenceto 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 Python, Rust, C++, or Go 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 (SGLang, vLLM, TensorRT-LLM, etc.)
• Open-source contributions in ML or systems infrastructure
• 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

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 (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.