About the role

SDET innovating and executing tests on cutting edge AI infrastructure for Cerebras. Championing cluster security and reliability for 99.9999% uptime and observability.

Responsibilities

You will be hired to innovate and execute tests on cutting edge AI infrastructure. Be a thinker, define optimized test strategies and methodologies.
Cerebras is growing and innovating at a rapid pace and so is the ML community and AI models. Be a quick learner, adapt to new technologies, and bring your expertise. We are looking to hire a team with a diverse skill set.
Deep understanding of how large-scale distributed ML training and inference works. Build a strong understanding of how to break these large distributed systems challenge into smaller components that can be unit tested.
Automate first approach - In large scale deployment, automation drives efficiency and scalability. Aim for 100% automated tests to test all cluster features in areas of high availability, failure scenarios, performance, stress and security.
Champion cluster security, reliability for uptime of 99.9999% and ease of use with observability.
Test all components of AI cluster including but not limited to cluster software involving kubernetes, prometheus and grafana. Cluster hardware components like ML wafer scale accelerators, CPU runtime nodes, High speed swarmx interconnect, High speed data transfer of weights through memoryx interconnect.

Bachelor's or master's degree in engineering in computer science, electrical, AI, data science or related field.
5+ years of experience in testing one of areas like enterprise software, distributed systems, datacenter hardware and software.
Strong coding skills in one of the programming languages like python, golang and C/C++.
Strong debugging skills to debug issues in large distributed systems, hardware, and software. Experience with debugging tools like pdb, gdb, strace and network monitors.
Strong understanding of operating systems internals like memory management, file system working, security and performance.
Strong understanding of datacenter layout, device performance characteristics like Servers, Memory, BIOS, PCIe, networking and storage.
Experience with cloud technologies like AWS, kubernetes and dockers. Monitoring tools like grafana, prometheus is huge plus.
Understanding and experience of ML model training and inference is a huge plus.
Understand of ML hardware accelerators like GPU, custom accelerator ASIC is a huge plus.