Lead AI Infrastructure Architect - GPU & Edge, Hybrid

Maxta, Inc.; California; Híbrido; USD 150.000 - 200.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flexible hybrid work model

Competitive salary

Equity in pre-IPO company

Conference budget

Continuous learning opportunities

Descripción de la vacante

Maxta, Inc. is seeking a Senior / Lead Engineer in California (Hybrid) to drive the technical vision behind GPU abstraction and intelligent compute scheduling. You will be responsible for architecting core systems, designing deployment pipelines, and mentoring the engineering team. Ideal candidates have over 8 years in distributed systems and expertise in GPU computing. This is an exciting opportunity to work directly with the CEO in a leading AI infrastructure company, offering a flexible hybrid work model with competitive salary and equity options.

Formación

• 8+ years in distributed systems, infrastructure, or platform engineering.
• Deep expertise in GPU computing and multi-GPU scheduling.
• Production experience with Kubernetes or custom scheduling systems.

Responsabilidades

• Lead the technical vision for heterogeneous GPU abstraction and compute scheduling.
• Architect MaxtaOS core systems for GPU resource management.
• Design MaxModel registry and deployment pipeline.

Conocimientos

GPU computing

Distributed systems

Infrastructure engineering

Systems programming in Python, Go, or C++

Kubernetes

Herramientas

CUDA

Docker

TorchServe

Triton

Descripción del empleo

Silicon Valley, CA (Hybrid) · Senior / Lead

You will lead the technical vision for heterogeneous GPU abstraction, intelligent compute scheduling, and model deployment pipelines that serve Fortune 500 enterprises.

What You Will Do

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

Requirements

• 8+ years in distributed systems, infrastructure, or platform engineering
• Deep expertise in GPU computing (CUDA, multi-GPU scheduling, heterogeneous hardware)
• Production experience with Kubernetes, container orchestration, or custom scheduling systems
• Strong background in Python, Go, or C++ for systems programming
• Experience with ML model serving (TorchServe, Triton, vLLM, or similar)
• Familiarity with edge computing platforms (NVIDIA Jetson, ARM64 deployment)
• Excellent system design and technical documentation skills
• Competitive salary + equity in a pre-IPO AI infrastructure company
• Work directly with the CEO on cutting-edge AI systems
• Backed by Andreessen Horowitz and Intel Capital
• 100+ patents portfolio — real deep-tech, not wrapper products
• Flexible hybrid work model (Silicon Valley + remote)
• Conference budget + continuous learning

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