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
Qualify cluster networking solutions which consists of high-speed switches, routers and optics from various vendors
Qualify cluster security features including OS security, network security, cloud compliance user access and security certifications
Qualifications
Bachelor's or master's degree in engineering in computer science, electrical, AI, data science of related field
10+ years of experience in testing one of areas like enterprise software, distributed systems, datacenter hardware and software
Experience working in large enterprise or cloud networking infrastructure, high speed switches, routers, firewalls
Experience in qualifying networking vendor platforms like Juniper, Arista or Cisco and network test equipment like Ixia/Spirent
Experience in Datacenter technology like BGP, ECN, PFC
Experience testing networking security, compliance and firewalls
Strong coding skills in one of the programming languages like python, golang or C/C++
Strong debugging skills to debug issues in large distributed systems, hardware, and software. Experience with debugging tools like gdb, strace, networking monitors
Strong understanding of operating systems internals like memory management, file system working, security basics and performance
Strong understanding of datacenter layout, device performance characteristics like 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
Why Join Cerebras
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:
1. Build a breakthrough AI platform beyond the constraints of the GPU.
2. Publish and open source their cutting-edge AI research.
3. Work on one of the fastest AI supercomputers in the world.
4. Enjoy job stability with startup vitality.
5. Our simple, non-corporate work culture that respects individual beliefs.
Find out more about what it's like to work at Cerebras here https://www.cerebras.ai/join-us!
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.
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