---
title: 'Member of Technical Staff — Developer Technology at RadixArk'
canonical: 'https://feeny.ai/job/member-of-technical-staff-developer-technology-radixark-palo-alto-vgg40r7f304a'
type: 'job'
last_seen: '2026-09-13'
---

# Member of Technical Staff — Developer Technology at RadixArk

- **Company:** RadixArk
- **Location:** Palo Alto, CA
- **Posted:** 2026-07-23
- **Last confirmed live:** 2026-09-13
- **Apply:** https://job-boards.greenhouse.io/radixark/jobs/4329271009

## Job description

## 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. 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 future roadmap.

## Key Responsibilities

- Accelerate AI workloads. Profile and optimize GPU performance for real production workloads on current and next-generation hardware, root-causing bottlenecks from kernels to distributed multi-node systems.

- Go deep in one or two focus areas. The team collectively covers the full stack; each engineer specializes in one or two tracks:

- Inference performance: engine tuning, benchmarking, long-context and multi-turn optimization, parallelism strategy, production debugging

- Kernels and model/hardware enablement: custom CUDA/ROCm/Triton kernels, low-precision quantization, day-0 support for new models on new silicon

- Speculative decoding: draft-model training, acceptance-rate tuning, cross-platform kernel adaptation

- Training systems: RL post-training with Miles, FP8 training, elasticity, long-rollout and long-context efficiency

- Partner directly with the ecosystem. Turn ambiguous, high-stakes problems from expert engineers at our key partners into concrete wins, clear technical guidance, and reproducible cookbooks.

- Enhance SGLang and Miles. Feed user-driven improvements back into our open-source systems and roadmap, so every win compounds across the ecosystem.

## Qualifications

## 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.

- Ability to translate ambiguous asks into clear technical plans, verified cookbooks, and actionable recommendations, and to communicate credibly with expert engineering audiences.

## Preferred (Bonus) Qualifications

- Deep familiarity with LLM inference internals: distributed serving, parallelism, routing, KV-cache management, scheduling.

- Experience with low-precision quantization and inference/training (FP8, INT8/INT4; NVFP4 or MXFP4 a strong plus).

- Experience writing and optimizing custom GPU kernels.

- Practical familiarity with speculative decoding methods such as Eagle, DFlash, or DSpark.

- Working knowledge of large-scale distributed training: pre-training, SFT, RL post-training, elasticity, long-context workloads.

- Experience optimizing across both NVIDIA and AMD platforms.

- Hands-on experience with SGLang, Miles, vLLM, TensorRT-LLM, Megatron, or comparable frameworks; contributions to open-source AI/ML projects.

## 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 RadixArk

## Company Overview
- **One-liner**: RadixArk builds open, large-scale inference and training systems to democratize access to frontier AI infrastructure for the entire AI community.
- **Entity Type**: Private (Seed stage)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2025
- **Founders**: Ying Sheng (CEO) and Banghua Zhu

## Core Business
- **Primary industry/industries**: AI Infrastructure, Deep-Tech, Open Source Software
- **Target customers**: Individual developers, startups, enterprises, and research labs building and deploying AI systems at scale.
- **Mission or purpose statement**: "Make frontier-level AI infrastructure open and accessible to everyone." [radixark.com](https://radixark.com/)

## Products & Services
- **[SGLang (Inference Engine)](https://radixark.com/)**: An open-source, high-performance serving engine for AI models. It has become a de facto standard, powering trillions of tokens daily for leaders like Google, Microsoft, NVIDIA, and xAI. RadixArk is the primary steward. **Type:** Open-Source Software / Managed Service
- **[Miles (RL Framework)](https://radixark.com/blog/radixark-launches-100m-seed)**: An open-source framework for large-scale reinforcement learning and post-training. Designed for efficiency and stability, it has been adopted for large-scale MoE training. **Type:** Open-Source Software
- **Managed Infrastructure & Tooling**: Built on top of SGLang and Miles, RadixArk offers managed services for the full lifecycle of AI development, from training to inference, for teams developing AI at scale. **Type:** Managed Service / Platform

## Market Standing
- **Valuation/Market Cap**: $400 million post-money valuation (as of May 2026) [cbinsights.com](https://www.cbinsights.com/company/radixark)
- **Key Metric**: $100 million total funding (Seed Round)
- **Notable Investors/Partners**: Accel (lead), Spark Capital (co-lead), NVentures (NVIDIA's venture arm), and participation from Google, Microsoft, and other strategic partners. [radixark.com](https://www.radixark.com/blog/radixark-launches-100m-seed)
- **Growth Signals**: The company has 19 employees as of mid-2026 but has seen a monthly headcount growth of +55.6%. Its open-source projects (SGLang and Miles) are already adopted by industry giants and research labs, indicating massive traction and developer mindshare. [linkedin.com](https://www.linkedin.com/company/radixark)

## Competitive Advantages
- **Open-Source Moat**: Its core products, SGLang and Miles, are open-source community standards with deep adoption. This creates a network effect and developer loyalty that is hard for proprietary competitors to replicate.
- **First-Principles Engineering Culture**: The company treats infrastructure engineering as a core creative discipline, not a support function, leading to deeper system-level innovations.
- **Top-Tier Talent & Backing**: Founded by the creators of SGLang and backed by Accel, Spark Capital, and NVIDIA, giving it immediate credibility, capital, and access to frontier hardware.
- **Clear Mission**: Focusing on making infrastructure "10x cheaper and 10x more accessible" directly addresses a massive pain point for the entire AI ecosystem. [radixark.com](https://www.radixark.com/)

## Strategic Focus
- **Growing SGLang & Miles**: Using the $100M seed to accelerate support for new model architectures, hardware platforms, and community improvements.
- **Building Managed Services**: Developing the managed infrastructure and tooling layer on top of its open-source cores to serve a wider range of customers.
- **Hiring Deeply Technical Talent**: Actively recruiting top-tier engineers for kernels, compilers, training systems, and TPU systems to maintain its engineering edge.
- **Expanding Global Reach**: With a Sales role based in Singapore, the company is signaling an intention to serve the APAC market.

## Why Work Here
- **Impactful Mission**: Employees contribute directly to making frontier AI infrastructure a shared foundation, working on problems that affect the entire AI ecosystem.
- **Engineering-Centric Culture**: The company explicitly states that "infrastructure engineering is not a support function," making it an ideal environment for deep-tech systems engineers who want to solve hard, first-principles problems. [radixark.com](https://www.radixark.com/blog/radixark-launches-100m-seed)
- **Work with Open Source at Scale**: You will be contributing to and stewarding widely-used open-source projects (SGLang, Miles) that power trillions of tokens daily.
- **Strong Backing & Early Stage**: Joining a well-funded seed-stage company with a $400M valuation offers significant upside and the chance to shape the company's culture and direction from an early stage.
- **Location & Hybrid Policy**: The company is headquartered in Palo Alto and San Francisco, California, with most roles listed as in-office. They also have a role in Singapore.
- **Notable Perks**: The company's blog emphasizes a culture of being "focused, humble, fearless, and meticulous," which appeals to mission-driven engineers.

## Sources
1. [RadixArk Official Website](https://radixark.com/)
2. [RadixArk Blog: $100M Seed Announcement](https://www.radixark.com/blog/radixark-launches-100m-seed)
3. [RadixArk Jobs on Greenhouse](https://job-boards.greenhouse.io/radixark)
4. [RadixArk LinkedIn Page](https://www.linkedin.com/company/radixark)
5. [CBInsights Company Profile](https://www.cbinsights.com/company/radixark)

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