AI Infrastructure Architect — GPU, Edge, Kubernetes

Huayun North America, Inc. (Maxta); Palo Alto (CA); Hybrid; USD 210,000 - 300,000; Full time

Benefits offered by this job

Equity in pre-IPO company

Hybrid work model (Sylcon Valley + REM

Health, dental, vision + 401K matching

Conference budget + continuouslearning

Patent portfolio exposure

Direct work with CEO

Job summary

Maxta is seeking an AI Infrastructure Architect to design the core MaxtaOS and MaxModel platforms in Palo Alto. You will drive the technical vision for heterogeneous GPU abstraction, intelligent compute scheduling, and model deployment pipelines for enterprise-scale workloads.

Lead the integration with edge devices, define API boundaries, and mentor the team while collaborating with leadership. This role offers equity, hybrid work in Silicon Valley, and exposure to cutting-edge AI systems.

Qualifications

• 8+ years in distributed systems, infrastructure, or platform engineering.
• Deep expertise in GPU computing (CUDA, multi-GPU scheduling, heterogeneous hardware).
• Production experience with Kubernetes or custom scheduling systems.
• Strong background in systems programming languages (Python, Go, or C++).

Responsibilities

• Architect MaxtaOS core systems: GPU resource management and multi-tenant scheduling.
• Design MaxModel registry and deployment pipeline: packaging, version control, AB testing infra.
• Lead integration with edge computing platforms (Jetson, custom CUDA kernels).
• Define API contracts and system boundaries across MaxtaOS+MaxModel stack.
• Mentor engineering team on distributed systems best practices.

Skills

Distributed systems

GPU computing

Python/Go/C++

Model serving

Edge computing

System design

Technical documentation

Tools

Kubernetes

TorchServe

Triton

Jetson

CUDA

Job description

Maxta is seeking an AI Infrastructure Architect to design the core MaxtaOS and MaxModel platforms in Palo Alto. You will drive the technical vision for heterogeneous GPU abstraction, intelligent compute scheduling, and model deployment pipelines for enterprise-scale workloads.

Lead the integration with edge devices, define API boundaries, and mentor the team while collaborating with leadership. This role offers equity, hybrid work in Silicon Valley, and exposure to cutting-edge AI systems.