Cerebras Systems
Senior Software Development Engineer in Test (SDET) - AI Cluster
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.
Cerebras' current customers include top 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.
Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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.
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.
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.
Qualifications
- 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.
People who are serious about software make their own hardware. At Cerebras we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
- Build a breakthrough AI platform beyond the constraints of the GPU.
- Publish and open source their cutting-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Enjoy job stability with startup vitality.
- Our simple, non-corporate work culture that respects individual beliefs.
Read our blog: Five Reasons to Join Cerebras in 2026.
Apply today and become part of the forefront of groundbreaking advancements in AI!Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
This website or its third-party tools process personal data. For more details, click here to review our CCPA disclosure notice.
Top Skills
Cerebras Systems Sunnyvale, California, USA Office
1237 E Arques Ave, Sunnyvale, CA, United States, 94085
Similar Jobs
What you need to know about the San Francisco Tech Scene
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
