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Deployment Engineer, AI Inference

United States; Canada, Sunnyvale · Job Posted February 17, 2026
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Job Description

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.

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

  • 2-5 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

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