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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. We are seeking a highly skilled and experienced Sr. Deployment Engineer to build and operate our cutting-edge inference clusters. These clusters would provide the candidate an opportunity to work with the world's largest computer chip, the Wafer-Scale Engine (WSE), and the systems that harness its unparalleled power. You will play a critical role in ensuring reliable, efficient, and scalable deployment of AI inference workloads across our global infrastructure.
Job Responsibility:
Deploy AI inference replicas and cluster software across multiple datacenters
Operate across heterogeneous datacenter environments undergoing rapid 10x growth
Maximize capacity allocation and optimize replica placement using constraint-solver algorithms
Operate bare-metal inference infrastructure while supporting transition to K8S-based platform
Develop and extend telemetry, observability and alerting solutions to ensure deployment reliability at scale
Develop and extend a fully automated deployment pipeline to support fast software updates and capacity reallocation at scale
Translate technical and customer needs into actionable requirements for the Dev Infra, Cluster, Platform and Core teams
Stay up to date with the latest advancements in AI compute infrastructure and related technologies.
Requirements:
5-7 years of experience in operating on-prem compute infrastructure (ideally in Machine Learning or High-Performance Compute) or in developing and managing complex AWS plane infrastructure for hybrid deployments
Strong proficiency in Python for automation, orchestration, and deployment tooling
Solid understanding of Linux-based systems and command-line tools
Extensive knowledge of Docker containers and container orchestration platforms like K8S
Familiarity with spine-leaf (Clos) networking architecture
Proficiency with telemetry and observability stacks such as Prometheus, InfluxDB and Grafana
Strong ownership mindset and accountability for complex deployments
Ability to work effectively in a fast-paced environment.
What we offer:
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.