Operates and develops software for large-scale AI compute clusters, improving reliability, capacity, monitoring, and incident response. Requires 6–8 years of complex infrastructure experience, strong Python and Go skills, distributed-systems expertise, and participation in 24/7 on-call support.
Build and operate the platform layer behind Cerebras engineering infrastructure, including CI/CD, Kubernetes, deployment automation, cloud and on-premises systems, developer environments, and observability. The role requires 5+ years of infrastructure or software engineering experience and strong debugging and systems fundamentals.
Build and maintain automation tooling for large-scale AI compute cluster deployments, turning bare-metal infrastructure into repeatable, pushbutton workflows using Python, Ansible, Terraform, Kubernetes and observability tools. Ideal for new graduates or early-career engineers seeking hands-on production infrastructure experience.
Infrastructure Engineer responsible for hands-on installation, provisioning, maintenance, and troubleshooting of high-performance on-premise server hardware, Linux systems, and high-speed networking (100G/400G) in a data center environment. Requires 3+ years experience with Linux admin, x86 hardware, and network configuration.
Build and maintain CI/CD pipelines, artifact management, cloud infrastructure, and developer productivity tooling at Cerebras to accelerate AI hardware and software engineering. Requires 2-5 years DevOps/infrastructure experience, Kubernetes, AWS, and strong troubleshooting skills.
Design, build, and operate secure cloud infrastructure and identity platforms in AWS and on-prem data centers. Implement IAM, automation with Terraform/Python/Go, security controls for AI systems, and Zero Trust principles while participating in on-call.
Principal SRE to architect self-service reliability platforms, capacity orchestration, and production control planes for Cerebras' ultra-high-speed AI inference infrastructure at massive scale. Requires 15+ years in SRE/platform engineering with deep large-scale fleet and observability experience.
Software engineer building and operating the orchestration layer for a globally distributed, high-performance AI inference platform on custom wafer-scale hardware.
Hands-on technical lead building and operating the orchestration layer for a globally distributed, high-performance AI inference platform on custom wafer-scale hardware.
Design and operate large-scale AI/HPC cluster network fabrics. Architect front-end datacenter interconnects, build automation and observability tooling, and debug complex distributed networking issues.
Develops and optimizes Kubernetes-based infrastructure for high-performance AI inference services, including deployment, scaling, debugging, and integration with ML workflows. Requires Master's in CS and 1+ year experience with Docker, Kubernetes, Python, and related tools.
Develops resilient, high-availability software for AI inference on AWS, including deployment workflows, container orchestration with Docker/Kubernetes, monitoring, and debugging. Requires Master's in CS and 18 months experience with AWS services, IaC tools, and Python.
Designs, implements, and optimizes global WAN networks using leased lines, dark fiber, and advanced routing protocols like BGP for low-latency, high-availability connectivity. Requires 6+ years experience, bachelor's degree, CCIE/JNCIE certs, and expertise in automation tools like Python/Ansible/Terraform.
Staff engineer owns architecture of Inference Cloud Platform, building distributed systems for high-QPS AI workloads with focus on availability, latency, reliability, and global scale. Requires 8+ years experience in large-scale cloud systems and backend languages like Go, C++, Python.
Principal Engineer leads Inference Cloud Platform, defining architecture for multi-region, high-QPS AI inference systems. Focuses on reliability, performance optimization, production code, and cross-team technical strategy. Requires 10+ years in distributed systems.
Leads automation and platform engineering for ultra-reliable AI inference infrastructure, architecting self-service GitOps pipelines, observability, and tooling to eliminate toil across datacenters. Requires 8+ years SRE experience with large-scale clusters and tools like Argo CD and Prometheus.
Leads end-to-end Design Validation Test (DVT) for complex electrical boards and systems, including power delivery, high-speed I/O validation, debug, and root-cause analysis. Requires 8+ years experience in hardware validation, strong EE skills, and lab equipment proficiency.
Entry-level SiteOps engineer supporting deployment, validation, monitoring, and first-line troubleshooting of AI clusters in data center environments. Requires a relevant engineering degree or equivalent experience, familiarity with server hardware, networking, and Linux, and readiness to work hands-on in data centers.
Build and operate distributed software for Cerebras wafer-scale AI clusters, including provisioning, orchestration, scheduling, monitoring, failure handling, and upgrade workflows. The role requires strong distributed-systems development experience and proficiency in Go, Python, Bash, Kubernetes, Prometheus, and Grafana.
Leads reliability strategy and hands-on implementation for a large-scale, low-latency AI inference service. The role requires 7+ years in backend, infrastructure, or reliability engineering, strong backend programming skills, and deep expertise in distributed-system reliability.
Operates and automates production infrastructure for a high-scale AI inference service. The role requires production Kubernetes experience, Python or Go proficiency, observability expertise, and a focus on reliability, automation, and reducing operational toil.
Operates and develops software for large-scale AI compute clusters, improving reliability, capacity, monitoring, and incident response. Requires 6–8 years of complex infrastructure experience, strong Python and Go skills, distributed-systems expertise, and participation in 24/7 on-call support.
Build and operate the platform layer behind Cerebras engineering infrastructure, including CI/CD, Kubernetes, deployment automation, cloud and on-premises systems, developer environments, and observability. The role requires 5+ years of infrastructure or software engineering experience and strong debugging and systems fundamentals.
Build and maintain automation tooling for large-scale AI compute cluster deployments, turning bare-metal infrastructure into repeatable, pushbutton workflows using Python, Ansible, Terraform, Kubernetes and observability tools. Ideal for new graduates or early-career engineers seeking hands-on production infrastructure experience.
Infrastructure Engineer responsible for hands-on installation, provisioning, maintenance, and troubleshooting of high-performance on-premise server hardware, Linux systems, and high-speed networking (100G/400G) in a data center environment. Requires 3+ years experience with Linux admin, x86 hardware, and network configuration.
Build and maintain CI/CD pipelines, artifact management, cloud infrastructure, and developer productivity tooling at Cerebras to accelerate AI hardware and software engineering. Requires 2-5 years DevOps/infrastructure experience, Kubernetes, AWS, and strong troubleshooting skills.
Design, build, and operate secure cloud infrastructure and identity platforms in AWS and on-prem data centers. Implement IAM, automation with Terraform/Python/Go, security controls for AI systems, and Zero Trust principles while participating in on-call.
Principal SRE to architect self-service reliability platforms, capacity orchestration, and production control planes for Cerebras' ultra-high-speed AI inference infrastructure at massive scale. Requires 15+ years in SRE/platform engineering with deep large-scale fleet and observability experience.
Software engineer building and operating the orchestration layer for a globally distributed, high-performance AI inference platform on custom wafer-scale hardware.
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Hands-on technical lead building and operating the orchestration layer for a globally distributed, high-performance AI inference platform on custom wafer-scale hardware.
Design and operate large-scale AI/HPC cluster network fabrics. Architect front-end datacenter interconnects, build automation and observability tooling, and debug complex distributed networking issues.
Develops and optimizes Kubernetes-based infrastructure for high-performance AI inference services, including deployment, scaling, debugging, and integration with ML workflows. Requires Master's in CS and 1+ year experience with Docker, Kubernetes, Python, and related tools.
Develops resilient, high-availability software for AI inference on AWS, including deployment workflows, container orchestration with Docker/Kubernetes, monitoring, and debugging. Requires Master's in CS and 18 months experience with AWS services, IaC tools, and Python.
Designs, implements, and optimizes global WAN networks using leased lines, dark fiber, and advanced routing protocols like BGP for low-latency, high-availability connectivity. Requires 6+ years experience, bachelor's degree, CCIE/JNCIE certs, and expertise in automation tools like Python/Ansible/Terraform.
Staff engineer owns architecture of Inference Cloud Platform, building distributed systems for high-QPS AI workloads with focus on availability, latency, reliability, and global scale. Requires 8+ years experience in large-scale cloud systems and backend languages like Go, C++, Python.
Principal Engineer leads Inference Cloud Platform, defining architecture for multi-region, high-QPS AI inference systems. Focuses on reliability, performance optimization, production code, and cross-team technical strategy. Requires 10+ years in distributed systems.
Leads automation and platform engineering for ultra-reliable AI inference infrastructure, architecting self-service GitOps pipelines, observability, and tooling to eliminate toil across datacenters. Requires 8+ years SRE experience with large-scale clusters and tools like Argo CD and Prometheus.
Leads end-to-end Design Validation Test (DVT) for complex electrical boards and systems, including power delivery, high-speed I/O validation, debug, and root-cause analysis. Requires 8+ years experience in hardware validation, strong EE skills, and lab equipment proficiency.
Entry-level SiteOps engineer supporting deployment, validation, monitoring, and first-line troubleshooting of AI clusters in data center environments. Requires a relevant engineering degree or equivalent experience, familiarity with server hardware, networking, and Linux, and readiness to work hands-on in data centers.
Build and operate distributed software for Cerebras wafer-scale AI clusters, including provisioning, orchestration, scheduling, monitoring, failure handling, and upgrade workflows. The role requires strong distributed-systems development experience and proficiency in Go, Python, Bash, Kubernetes, Prometheus, and Grafana.
Leads reliability strategy and hands-on implementation for a large-scale, low-latency AI inference service. The role requires 7+ years in backend, infrastructure, or reliability engineering, strong backend programming skills, and deep expertise in distributed-system reliability.
Operates and automates production infrastructure for a high-scale AI inference service. The role requires production Kubernetes experience, Python or Go proficiency, observability expertise, and a focus on reliability, automation, and reducing operational toil.