Senior Software Development Engineer in Test AI Cluster
Summary
Senior SDET at Cerebras defining test strategies and automating validation for large-scale AI cluster infrastructure — breaking distributed ML training/inference systems into testable components and testing availability, failures, performance, stress, and security. Core tech: Python, Go, or C/C++, plus debugging tools like pdb, gdb, and strace.
You will define test strategies and automate validation for large-scale AI infrastructure. You will break distributed ML training and inference systems into testable components, test cluster availability, failures, performance, stress, and security, and improve observability and reliability. You will validate cluster software, hardware, networking, monitoring, and deployment components while debugging distributed hardware and software issues.
Responsibilities
- Define and execute optimized test strategies and methodologies for AI infrastructure
- Adapt testing approaches to new technologies and AI models
- Break large distributed ML training and inference systems into unit-testable components
- Automate tests for cluster availability, failure scenarios, performance, stress, and security
- Champion cluster security, reliability, uptime, and observability
- Test AI cluster software, hardware, interconnects, and monitoring components
Requirements
- Bachelor's or master's degree in computer science, electrical engineering, AI, data science, or a related field
- 5+ years of experience testing enterprise software, distributed systems, or datacenter hardware and software
- Strong coding skills in Python, Go, or C/C++
- Strong debugging skills for large distributed hardware and software systems
- Experience with debugging tools such as pdb, gdb, strace, and network monitors
- Understanding of operating-system internals, memory management, file systems, security, and performance
- Understanding of datacenter layout and server, memory, BIOS, PCIe, networking, and storage characteristics
