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Senior Distributed Systems Engineer

United States
Visa Sponsorship
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AI Summary

Design and build a high-performance AI inference platform's scheduling and routing layer. Develop a system that efficiently places work across a heterogeneous multi-cloud GPU fleet. Work independently to own systems end-to-end.

Key Highlights
Design and build a high-performance AI inference platform's scheduling and routing layer
Develop a system that efficiently places work across a heterogeneous multi-cloud GPU fleet
Work independently to own systems end-to-end

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Key Responsibilities
Own the scheduling and routing layer — design and build request admission, prioritization, batching, and placement across a heterogeneous GPU fleet spanning multiple clouds and accelerator types
Engineer for latency and utilization at once — drive down tail latency while driving up fleet utilization
Model the system, not just code it — apply queueing theory, control theory, and load-shedding principles to make the platform behave predictably under bursty, multi-tenant traffic
Technical Skills Required
Go Rust GPU infrastructure

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Benefits & Perks
Meaningful equity
Medical, dental, and vision coverage
Other benefits

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Nice to Have
Kubernetes and multi-cloud operations experience
Open-source contributions to inference, serving, or scheduling projects
Experience operating GPU clusters at scale

Job Description

NeuroSpark builds and operates a high-performance AI inference platform that helps enterprises run large language models faster, cheaper, and at scale. Inference infrastructure is the foundation the entire AI application layer runs on — every AI product ultimately depends on how fast, how reliably, and how affordably models can serve their users. Our vision is to make that layer so efficient that compute is never the reason a good AI product fails.

About the Role

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Job Overview
Posted Aug 18, 2026
Employment Full-time
Experience Mid-Senior level
Location United States
Category Programming
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