Senior/Staff Backend Engineer - Distributed System jobs in United States
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Zettabyte · 7 hours ago

Senior/Staff Backend Engineer - Distributed System

Zettabyte is on a mission to make AI compute ubiquitous, seamless, and limitless. They are seeking a Backend Engineer to build systems that orchestrate GPU clusters for AI workloads, creating APIs and resource management systems that directly impact the efficiency of AI infrastructure.

Artificial Intelligence (AI)Cloud ComputingSoftware
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Responsibilities

Design APIs that abstract complex GPU operations into simple developer experiences
Build scheduling algorithms that maximize GPU utilization while ensuring SLA compliance
Develop resource management systems for GPU lifecycle—provisioning, allocation, scheduling, and release
Create usage tracking and billing systems for GPU-hours, memory usage, and compute utilization
Implement monitoring for GPU-specific metrics, health checks, and automatic failure recovery
Build multi-tenancy systems with resource isolation, quota management, and fair scheduling
Optimize cold starts for model serving and implement efficient model loading strategies
Collaborate with frontend engineers to expose complex infrastructure through intuitive interfaces
Leverage AI-assisted coding tools (GitHub Copilot, Claude Code, Cursor IDE, etc.) to boost productivity and code quality

Qualification

Backend engineeringDistributed systemsGoPythonAPI designResource schedulingLinux systemsContainerizationGPU managementPerformance optimizationStartup mindset

Required

5+ years backend engineering experience with distributed systems
Strong proficiency in Go, Python, or similar backend languages
Experience with resource scheduling, orchestration, and API design (REST, GraphQL, gRPC)
Understanding of hardware constraints and system optimization
Linux systems knowledge and containerization experience (Docker, Kubernetes)
Comfortable working with expensive resources where efficiency directly impacts costs
Excited about solving novel problems in AI infrastructure (not just another CRUD app)
Startup mindset—comfortable with ambiguity and rapid iteration

Preferred

GPU or HPC cluster management experience
Understanding of ML/AI workload patterns and requirements
Experience with high-value resource allocation systems
Background in performance optimization for compute-intensive workloads
Familiarity with GPU virtualization and sharing technologies
Experience building billing or metering systems

Company

Zettabyte

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Zettabyte offers an AI-native platform that manages and scales GPU workloads efficiently using its intelligent orchestration system.

Funding

Current Stage
Early Stage
Total Funding
unknown
Key Investors
Lam Capital
2025-08-01Series Unknown
Company data provided by crunchbase