Solutions Architect III, Devices Technologies (Fulfillment Technology & Robotics)
We're looking for a Head of Business Development to build the BD function at RadixArk from the ground up. The BD team is the institutional memory of this company — maintaining active relationships across every customer we've touched, detecting signals early, and ensuring nothing falls through the cracks. You'll design the system, build the team, and run it.
The role is equal parts relationship and rigor. You'll hold the relationship thread across a broad customer surface while tracking coverage rates, handoff volume, signal trends, and engagement quality — and translating that into clear intelligence for the broader commercial organization.
We move fast, operate with a sense of urgency, and expect the same from everyone on the team.
Team Building
Relationship Coverage
Downstream Handoff
Strategy & Intelligence
Strategic Partnerships
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.
We offer competitive compensation with equity, comprehensive health benefits, and flexible work arrangements. Compensation is determined by location, level, and experience.
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.
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RadixArk builds large-scale training and inference systems, with the aim of making frontier-level AI infrastructure open and accessible.
The work is systems-heavy: distributed training, serving throughput, and the software layer between models and accelerators.
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