Software Development Engineer in Test - AI

Role overview Build the testing foundation for infrastructure that supports distributed AI training and inference. The role focuses on automation, integration, and stress testing across distributed systems, GPU kernels, and AI frameworks, helping teams find and diagnose reliability and performance issues at scale.

Responsibilities - Design, build, and maintain an automated test framework for distributed AI workloads. - Create end-to-end integration and stress tests beyond unit-level coverage. - Test conditions at the boundary between hardware and software. - Partner with system engineers to investigate failures in distributed clusters. - Use telemetry and profiling tools to help identify system bottlenecks.

Requirements - At least five years of relevant experience. - Strong Python skills for test automation and orchestration. - Experience building or extending automation frameworks for complex backend systems. - Ability to troubleshoot test failures in large distributed systems and identify root causes. - Experience with containers such as Docker or Kubernetes and CI/CD tools such as GitHub Actions, GitLab CI, or Jenkins. - Bachelor's or master's degree in computer engineering, computer science, or a related field.

Nice to have - Experience using Kubernetes, Terraform, or Ansible to manage test environments. - Experience testing high-performance networking protocols or distributed file systems. - Experience testing software that interacts directly with drivers or firmware.

Benefits and work setup - Hybrid role based in Palo Alto, California. - Advertised target base salary of $140,000–$220,000 per year, plus equity, benefits, and a 401(k). The range depends on role level, experience, and location.