SproutsAI
Senior, Staff Backend Engineer - Distributed System
SproutsAI, Palo Alto, California, United States, 94306
Actively hiring
Job type :
Full-time
Workplace type :
Hybrid
Experience :
5 years
About Us At Zettabyte, we’re on a mission to make AI compute ubiquitous, seamless, and limitless. We’re building a cloud where AI just works—anywhere, anytime. “AI Power. Everywhere.” Be part of the team designing the infrastructure for the AI-first world.
Why this role exists We need a Backend Engineer to build the systems that orchestrate GPU clusters for AI workloads. You'll create APIs that handle GPU allocation, memory management, compute scheduling, and multi-tenant isolation—challenges unique to AI infrastructure that go far beyond typical backend engineering. As part of our backend team, you'll solve problems like
How do we efficiently share expensive GPU resources across users?
How do we handle GPU memory constraints for large AI models?
How do we ensure quality of service when workloads compete for compute?
This is an opportunity to build infrastructure where every API call could allocate thousands of dollars worth of compute per hour, where your optimizations directly impact whether AI startups can afford to train their models.
What you`ll do
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.
You`ll thrive here if you
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
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
Details We provide Competitive salary and equity based on your experience and skillset;
This is a Hybrid role - 3 days in office, 2 days WFH; Must locate in Palo Alto
Applicants must be authorized to work in the United States without need for visa sponsorship.
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Job type :
Full-time
Workplace type :
Hybrid
Experience :
5 years
About Us At Zettabyte, we’re on a mission to make AI compute ubiquitous, seamless, and limitless. We’re building a cloud where AI just works—anywhere, anytime. “AI Power. Everywhere.” Be part of the team designing the infrastructure for the AI-first world.
Why this role exists We need a Backend Engineer to build the systems that orchestrate GPU clusters for AI workloads. You'll create APIs that handle GPU allocation, memory management, compute scheduling, and multi-tenant isolation—challenges unique to AI infrastructure that go far beyond typical backend engineering. As part of our backend team, you'll solve problems like
How do we efficiently share expensive GPU resources across users?
How do we handle GPU memory constraints for large AI models?
How do we ensure quality of service when workloads compete for compute?
This is an opportunity to build infrastructure where every API call could allocate thousands of dollars worth of compute per hour, where your optimizations directly impact whether AI startups can afford to train their models.
What you`ll do
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.
You`ll thrive here if you
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
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
Details We provide Competitive salary and equity based on your experience and skillset;
This is a Hybrid role - 3 days in office, 2 days WFH; Must locate in Palo Alto
Applicants must be authorized to work in the United States without need for visa sponsorship.
#J-18808-Ljbffr