Why this role existsWe 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
Bonus qualifications- 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
