Senior Software Development Engineer in Test (SDET) - AI Cluster

Cerebras Systems•Toronto, ON

29d

About The Position

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. In AI infrastructure organization, simplifying large hardware deployments with push button, single pane of glass for observability/monitoring and software capabilities for build-in resiliency are some of the key focus areas. As senior software development engineer in Test, we are looking for a candidate who can make a big impact on how we test and validate thousands of nodes in large deployments to ensure the cluster is 99.999% reliable.

Requirements

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.

Nice To Haves

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.

Responsibilities

Innovate and execute tests on cutting edge AI infrastructure.
Define optimized test strategies and methodologies.
Adapt to new technologies and bring expertise.
Deep understanding of how large-scale distributed ML training and inference works.
Break down large distributed systems challenges into smaller components that can be unit tested.
Automate 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.
Test cluster hardware components like ML wafer scale accelerators, CPU runtime nodes, High speed swarmx interconnect, High speed data transfer of weights through memoryx interconnect.