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Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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. Cerebras works with the leading 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. About The Role The Host and Network IO Team develops the full IO path implementation between a distributed system of server nodes, through the cluster, down to the custom RoCE network stack implemented in Cerebras' system, and over the proprietary IOs onto the WSE. As a software developer on the team, you will interface between AI application-level IO teams, cluster architecture teams, and FPGA/ASIC teams to develop solutions that optimize bandwidth and latency while minimizing congestion, pauses, pause spreading, unfairness, etc. Strong skills in socket programming will enable you to deploy robust management operations, while deftness in RDMA Verbs will enable you to optimize CPU resources and shape network traffic to deliver real world impact on AI performance metrics, as well as developing tools for gaining insight and visibility into network behavior. Meticulous analysis and rigour are key tenants of this role, harnessing that together with a deeply-understood mental model of the server, NIC, protocol, switch, and custom hardware behavior will enable you to lead network debug, optimize traffic patterns, and prescribe architectural changes. Responsibilities Develop x86 & ARM software to expose next-generation hardware IO capabilities for AI/HPC application teams Govern a generic IO API with multiple internal users. Develop control and configuration subsystems directly interacting with Cerebras hardware Drive network performance debug of large AI clusters Gather and analyze network statistics and packet traces to root cause and alleviate bottlenecks and sub-optimalities. Develop tools/telemetry for increasing visibility into the network and IO datapath. Optimize cpu/mem utilization leveraging kernel bypass and zero-copy techniques Integrate leading edge networking technologies and protocols Lead cross-functional technical projects spanning multiple teams and integrating diverse software and hardware components to deliver an improved network IO solution. Foster clear and effective communication across teams and stakeholders. Skills & Qualifications Master's/PhD in Computer Science or Electrical Engineering + 1 year industry experience, OR 3+ years industry experience. Experience in large software environments. Embedded systems, HW/SW co-design, and some driver development. Network protocol familiarity (TCP, RoCE) and network debug tools such as Wireshark, or willingness to learn Some network switch environment familiarity or willingness to learn (Arista, Juniper, etc.). Detail-oriented but keen to learn the bigger picture and step out of comfort zone to embrace the unknown. 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: 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. Find out more about what it's like to work at Cerebras here ! 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.

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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. Cerebras works with the leading 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. About The Role The Host and Network IO Team develops the full IO path implementation between a distributed system of server nodes, through the cluster, down to the custom RoCE network stack implemented in Cerebras' system, and over the proprietary IOs onto the WSE. As a software developer on the team, you will interface between AI application-level IO teams, cluster architecture teams, and FPGA/ASIC teams to develop solutions that optimize bandwidth and latency while minimizing congestion, pauses, pause spreading, unfairness, etc. Strong skills in socket programming will enable you to deploy robust management operations, while deftness in RDMA Verbs will enable you to optimize CPU resources and shape network traffic to deliver real world impact on AI performance metrics, as well as developing tools for gaining insight and visibility into network behavior. Meticulous analysis and rigour are key tenants of this role, harnessing that together with a deeply-understood mental model of the server, NIC, protocol, switch, and custom hardware behavior will enable you to lead network debug, optimize traffic patterns, and prescribe architectural changes. Responsibilities Develop x86 & ARM software to expose next-generation hardware IO capabilities for AI/HPC application teams Govern a generic IO API with multiple internal users. Develop control and configuration subsystems directly interacting with Cerebras hardware Drive network performance debug of large AI clusters Gather and analyze network statistics and packet traces to root cause and alleviate bottlenecks and sub-optimalities. Develop tools/telemetry for increasing visibility into the network and IO datapath. Optimize cpu/mem utilization leveraging kernel bypass and zero-copy techniques Integrate leading edge networking technologies and protocols Lead cross-functional technical projects spanning multiple teams and integrating diverse software and hardware components to deliver an improved network IO solution. Foster clear and effective communication across teams and stakeholders. Skills & Qualifications Master's/PhD in Computer Science or Electrical Engineering + 1 year industry experience, OR 3+ years industry experience. Experience in large software environments. Embedded systems, HW/SW co-design, and some driver development. Network protocol familiarity (TCP, RoCE) and network debug tools such as Wireshark, or willingness to learn Some network switch environment familiarity or willingness to learn (Arista, Juniper, etc.). Detail-oriented but keen to learn the bigger picture and step out of comfort zone to embrace the unknown. 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: 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. Find out more about what it's like to work at Cerebras here ! 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.

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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. Cerebras works with the leading 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. About the Role As a Staff GPU Inference SDET, you will be the founding quality, reliability, and validation lead for a new GPU Inference Development team. Working closely with engineering leads and cross-functional systems infrastructure teams, you will design, build, and scale the end-to-end release qualification and automated test ecosystem for our GPU inference stack and rack-scale accelerated compute fleets. In this high-impact role, you will be responsible for building automated test suites to validate multi-node GPU cluster bring-up, verifying prefill worker optimizations, testing open-source and custom serving engines, and ensuring numerical correctness and performance stability under real-world streaming workloads. You will be the primary technical anchor ensuring production-grade reliability, fault isolation, and peak inference performance across accelerated GPU infrastructure. WHAT YOU’LL DO Build GPU Release Qualification Systems : Design and implement automated test automation frameworks, regression gates, and release qualification pipelines for the complete GPU inference stack—spanning custom API services, model-serving workers, container runtimes, serving engines, driver stacks, and firmware. Inference Serving & Workload Validation : Benchmark and stress-test distributed LLM serving frameworks, focusing on prefill vs. decode worker performance, continuous batching, prefix caching, KV-cache efficiency, and tensor/expert parallelism. Performance & Performance Modeling Verification : Build automated workload replay and benchmarking tools to validate GPU performance models. Track critical serving metrics including Time-to-First-Token (TTFT), Inter-Token Latency (ITL), request throughput, tail latency (P99), and capacity efficiency. Numerical Correctness & Quality Gates : Build validation infrastructure to ensure model accuracy, precision stability (FP16/FP8/quantization), determinism, and output correctness across software updates, kernel fusions, and hardware revisions. Fault Injection & Fleet Resilience : Engineer chaos engineering and fault-injection suites to simulate node failures, inter-node network degradation, GPU memory leaks, driver/firmware mismatches, and automated recovery paths for multi-node GPU clusters. Observability & CI/CD Integration : Integrate automated test pipelines with telemetry tools (e.g., Prometheus, Grafana) to turn one-off investigations into repeatable engineering gates and continuous performance monitoring. REQUIREMENTS: 8+ years of software engineering experience as an SDET, Infrastructure Quality Lead, or Systems Test Engineer. GPU & Cluster Infrastructure Expertise : Hands-on experience bringing up, provisioning, and validating multi-node GPU clusters (NVIDIA or AMD ecosystem) across public cloud infrastructure or enterprise data center environments. Inference Stack Knowledge : Deep understanding of LLM serving engines and distributed runtimes, including prefill vs. decode disaggregation, KV-cache management, and dynamic batching. Automation & Scripting : Expert-level Python programming skills with extensive experience designing custom test automation frameworks, diagnostic tooling, and CI/CD integration. Orchestration & Networking : Strong proficiency with container orchestration tools (e.g., Kubernetes, Slurm, Ray) and high-performance cluster interconnects (e.g., InfiniBand, RoCE, NCCL). Failure Analysis & Debugging : Proven background in root-cause analysis across software/hardware boundaries, stress testing, and node failure simulation in distributed systems. NICE TO HAVES: Direct experience with either AMD (ROCm / HIP) or NVIDIA software stacks. Experience building workload replay tools, ML evaluation pipelines, or MLPerf Inference benchmark suites. Familiarity with low-level kernel profiling tools (PyTorch Profiler, NVTX, ROCm profilers) or C++ 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: 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. Find out more about what it's like to work at Cerebras here ! 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.

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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. Cerebras works with the leading 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. About the Role As a Staff GPU Inference SDET, you will be the founding quality, reliability, and validation lead for a new GPU Inference Development team. Working closely with engineering leads and cross-functional systems infrastructure teams, you will design, build, and scale the end-to-end release qualification and automated test ecosystem for our GPU inference stack and rack-scale accelerated compute fleets. In this high-impact role, you will be responsible for building automated test suites to validate multi-node GPU cluster bring-up, verifying prefill worker optimizations, testing open-source and custom serving engines, and ensuring numerical correctness and performance stability under real-world streaming workloads. You will be the primary technical anchor ensuring production-grade reliability, fault isolation, and peak inference performance across accelerated GPU infrastructure. WHAT YOU’LL DO Build GPU Release Qualification Systems : Design and implement automated test automation frameworks, regression gates, and release qualification pipelines for the complete GPU inference stack—spanning custom API services, model-serving workers, container runtimes, serving engines, driver stacks, and firmware. Inference Serving & Workload Validation : Benchmark and stress-test distributed LLM serving frameworks, focusing on prefill vs. decode worker performance, continuous batching, prefix caching, KV-cache efficiency, and tensor/expert parallelism. Performance & Performance Modeling Verification : Build automated workload replay and benchmarking tools to validate GPU performance models. Track critical serving metrics including Time-to-First-Token (TTFT), Inter-Token Latency (ITL), request throughput, tail latency (P99), and capacity efficiency. Numerical Correctness & Quality Gates : Build validation infrastructure to ensure model accuracy, precision stability (FP16/FP8/quantization), determinism, and output correctness across software updates, kernel fusions, and hardware revisions. Fault Injection & Fleet Resilience : Engineer chaos engineering and fault-injection suites to simulate node failures, inter-node network degradation, GPU memory leaks, driver/firmware mismatches, and automated recovery paths for multi-node GPU clusters. Observability & CI/CD Integration : Integrate automated test pipelines with telemetry tools (e.g., Prometheus, Grafana) to turn one-off investigations into repeatable engineering gates and continuous performance monitoring. REQUIREMENTS: 8+ years of software engineering experience as an SDET, Infrastructure Quality Lead, or Systems Test Engineer. GPU & Cluster Infrastructure Expertise : Hands-on experience bringing up, provisioning, and validating multi-node GPU clusters (NVIDIA or AMD ecosystem) across public cloud infrastructure or enterprise data center environments. Inference Stack Knowledge : Deep understanding of LLM serving engines and distributed runtimes, including prefill vs. decode disaggregation, KV-cache management, and dynamic batching. Automation & Scripting : Expert-level Python programming skills with extensive experience designing custom test automation frameworks, diagnostic tooling, and CI/CD integration. Orchestration & Networking : Strong proficiency with container orchestration tools (e.g., Kubernetes, Slurm, Ray) and high-performance cluster interconnects (e.g., InfiniBand, RoCE, NCCL). Failure Analysis & Debugging : Proven background in root-cause analysis across software/hardware boundaries, stress testing, and node failure simulation in distributed systems. NICE TO HAVES: Direct experience with either AMD (ROCm / HIP) or NVIDIA software stacks. Experience building workload replay tools, ML evaluation pipelines, or MLPerf Inference benchmark suites. Familiarity with low-level kernel profiling tools (PyTorch Profiler, NVTX, ROCm profilers) or C++ 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: 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. Find out more about what it's like to work at Cerebras here ! 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.

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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. Cerebras works with the leading 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. Cerebras is seeking a Network Security Engineer to build, operate, and secure the network infrastructure supporting our data centers, cloud environments, corporate network, and AI systems. This is a hands-on individual contributor role based in Sunnyvale, CA , with regular on-site work in our data centers. You will work across network security and network operations, supporting firewalls, segmentation, routing and switching, network access, and automation. Responsibilities Operate firewalls, ACLs, segmentation, and network security controls across data center, corporate, and AWS environments. Support hands-on data center network operations, including deployment, configuration, troubleshooting, maintenance, and lifecycle management. Implement and maintain segmentation, firewall, and ACL policies across corporate, compute, customer, and infrastructure environments. Manage inbound and outbound network controls, including public exposure, NAT, load balancers, DNS filtering, proxies, and egress policies. Implement and operate VPN, ZTNA, NAC, Wi-Fi, and vendor or partner connectivity. Perform recurring firewall rule reviews, segmentation audits, and remediation of unnecessary network exposure. Automate network and security workflows using Terraform, Ansible, GitOps, Python, and policy as code. Support network telemetry, detection, investigation, and containment in partnership with Security Operations. Troubleshoot routing, switching, TCP/IP, DNS, TLS, and cloud networking issues. Maintain network architecture documentation, procedures, and runbooks. Skills and Qualifications 7+ years of experience in network security, network engineering, cloud security, or infrastructure security. Strong hands-on experience with firewalls, ACLs, segmentation, routing, switching, VPNs, and network troubleshooting. Experience operating data center and on-premises network infrastructure. Experience with AWS networking, including VPCs, transit gateways, security groups, and load balancers. Experience with Palo Alto, Juniper, Cloudflare, or similar platforms. Proficiency with Terraform, Ansible, Python, GitOps, or similar automation tools. Strong understanding of ZTNA, NAC, egress controls, TCP/IP, DNS, and TLS. Strong written communication and documentation skills. Ability to work regularly on-site in Sunnyvale, CA and perform hands-on work in data center environments. Relevant Experience Experience in several of the following is valuable: AI, HPC, or large-scale compute environments. Data center networking and security. AWS network security and segmentation. ZTNA and VPN architectures. DNS filtering, SWG, proxy, or SASE platforms. Vendor and partner connectivity. Public exposure and attack surface remediation. Network detection and incident response. 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: 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. Find out more about what it's like to work at Cerebras here ! 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.

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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. Cerebras works with the leading 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. The Role The Cluster engineering team owns the software that turns thousands of wafers, servers, and switches into a cloud that stays up, stays busy, and stays debuggable. We stand clusters up from bare metal, schedule training and inference workloads across the fleet, keep it healthy, and make it observable to users, operators, and increasingly to AI agents. The stack is Go and Python on Kubernetes, running both on-premise deployments and our own cloud. Responsibilities · Declarative, CRD-driven automation of bare-metal networking, OS, and application software across clusters of Cerebras systems, servers, and switches, built to reconcile thousands of nodes · Push-button cluster install, upgrade, and security patching with real downtime budgets, gated by canaries · Kubernetes operators that schedule large inference workload: resource locks, priority queues, network topology, and health-aware placement · gRPC control-plane services, authorization, admission webhooks, and quota policy for a multi-tenant fleet · Metrics and log pipelines with purpose-built exporters for wafer-scale systems, servers (Redfish, IPMI), and network fabric (gNMI, sFlow), on Prometheus and Grafana, with SLOs and alerting · Failure detection, HA control planes, and automated recovery, plus the CLIs, APIs, and MCP gateway that expose the fleet to users, operators, and AI agents Skills and Qualifications · 5+ years building and operating production distributed systems or infrastructure software · Production-quality Go and Python · Real Kubernetes depth: you have written or debugged controllers and operators, and you understand CRDs, reconciliation semantics, informer caches, admission webhooks, and RBAC · Strong debugging skills across distributed systems, Linux, and networking · Prometheus and Grafana as a practitioner: PromQL, exporter design, cardinality discipline, useful alerts · Strong self-driving capability. This environment is large, fast-moving, and not fully documented, so we need engineers who build their own context, decide, and drive work across team boundaries. Learning speed matters more here than familiarity with our stack. · Demonstrated adoption of AI in your engineering workflow: active use of coding agents, a view on where they help and where they mislead, and the rigor to verify what they produce. · Nice to have: bare-metal or HPC fleet operations, scheduler internals, RDMA/RoCE and eBPF networking, Ceph or NVMe-oF, etcd and HA upgrades, inference serving stacks. ML research experience is not required. 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: 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. Find out more about what it's like to work at Cerebras here ! 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.

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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. Cerebras works with the leading 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. About the Team The Core Infrastructure team builds the software systems that power engineering workflows across Cerebras. Our infrastructure coordinates complex work across machines, clusters, development environments, and hardware systems. We build orchestration frameworks, execution engines, scheduling systems, test infrastructure, developer tools, and reusable software platforms that allow engineers to build, test, qualify, and deliver software reliably at scale. These systems are primarily built in Python, but the work goes far beyond scripting or automation. Our frameworks act as the control plane for distributed workflows, managing resources, execution state, concurrency, failures, retries, dependencies, and observability across large and complex environments. About the Role We are hiring a Software Engineer to build and operate the platform layer behind Cerebras engineering infrastructure. You will work on CI/CD systems, Kubernetes, deployment automation, cloud and on-premises infrastructure, developer environments, artifact management, and observability. You will help make the systems engineers depend on reliable, scalable, and easy to operate. This is an engineering-focused infrastructure role rather than a primarily ticket-driven operations position. You will automate repeated work, debug failures across system boundaries, and turn operational problems into durable software and platform improvements. We value strong systems fundamentals, independent problem solving, and sound engineering judgment more than familiarity with any particular infrastructure product. Responsibilities Design, build, and maintain CI/CD systems supporting build, test, integration, qualification, and release workflows. Build and operate Kubernetes-based platforms and services used by engineering teams across Cerebras. Develop deployment systems, internal tools, and self-service workflows that make infrastructure changes repeatable, reviewable, and safe. Improve infrastructure reliability, capacity, performance, cost efficiency, monitoring, and operational readiness. Debug issues spanning CI pipelines, Kubernetes workloads, networking, storage, authentication, operating systems, and distributed applications. Perform root-cause analysis and implement lasting fixes rather than relying on repeated manual intervention. Partner with software, IT, security, networking, release, and developer-productivity teams to deliver scalable infrastructure solutions. Skills & Qualifications 5+ years of professional experience in platform engineering, DevOps, infrastructure engineering, site reliability engineering, or software engineering. Hands-on experience building or maintaining CI/CD pipelines and automated software-delivery workflows. Experience deploying and operating services using Kubernetes and containerized environments. Experience with a major cloud platform, preferably AWS, and programmatic infrastructure provisioning. Strong understanding of Linux or Unix operating-system fundamentals. Understanding of networking concepts such as DNS, routing, load balancing, proxies, ports, TLS, and service connectivity. Proficiency in Python, Shell, or another language used to build infrastructure automation and operational tooling. Experience with monitoring, logging, alerting, dashboards, and incident investigation. Strong debugging and problem-solving skills, including the ability to investigate issues spanning applications, infrastructure, networking, and operating systems. Preferred Skills & Qualifications Experience with infrastructure-as-code tools, specifically Terraform. Experience with Kubernetes controllers, operators, custom resources, Helm, Argo CD, or similar platform technologies. Experience managing artifact repositories, package registries, build caches, or software-distribution infrastructure. Familiarity with build systems, dependency management, and reproducible-build practices. Experience supporting hybrid environments spanning cloud infrastructure, on-premises systems, and specialized hardware. Experience with identity and access management, secrets, certificates, TLS, or mTLS. Experience building internal developer platforms or self-service infrastructure products. BS/MS in Computer Science or a related field, or equivalent practical experience. 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: 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. Find out more about what it's like to work at Cerebras here ! 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.

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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. Cerebras works with the leading 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. About the Team The Core Infrastructure team builds the software systems that power engineering workflows across Cerebras. Our infrastructure coordinates complex work across machines, clusters, development environments, and hardware systems. We build orchestration frameworks, execution engines, scheduling systems, test infrastructure, developer tools, and reusable software platforms that allow engineers to build, test, qualify, and deliver software reliably at scale. These systems are primarily built in Python, but the work goes far beyond scripting or automation. Our frameworks act as the control plane for distributed workflows, managing resources, execution state, concurrency, failures, retries, dependencies, and observability across large and complex environments. About the Role We are hiring a Software Engineer to build and operate the platform layer behind Cerebras engineering infrastructure. You will work on CI/CD systems, Kubernetes, deployment automation, cloud and on-premises infrastructure, developer environments, artifact management, and observability. You will help make the systems engineers depend on reliable, scalable, and easy to operate. This is an engineering-focused infrastructure role rather than a primarily ticket-driven operations position. You will automate repeated work, debug failures across system boundaries, and turn operational problems into durable software and platform improvements. We value strong systems fundamentals, independent problem solving, and sound engineering judgment more than familiarity with any particular infrastructure product. Responsibilities Design, build, and maintain CI/CD systems supporting build, test, integration, qualification, and release workflows. Build and operate Kubernetes-based platforms and services used by engineering teams across Cerebras. Develop deployment systems, internal tools, and self-service workflows that make infrastructure changes repeatable, reviewable, and safe. Improve infrastructure reliability, capacity, performance, cost efficiency, monitoring, and operational readiness. Debug issues spanning CI pipelines, Kubernetes workloads, networking, storage, authentication, operating systems, and distributed applications. Perform root-cause analysis and implement lasting fixes rather than relying on repeated manual intervention. Partner with software, IT, security, networking, release, and developer-productivity teams to deliver scalable infrastructure solutions. Skills & Qualifications 5+ years of professional experience in platform engineering, DevOps, infrastructure engineering, site reliability engineering, or software engineering. Hands-on experience building or maintaining CI/CD pipelines and automated software-delivery workflows. Experience deploying and operating services using Kubernetes and containerized environments. Experience with a major cloud platform, preferably AWS, and programmatic infrastructure provisioning. Strong understanding of Linux or Unix operating-system fundamentals. Understanding of networking concepts such as DNS, routing, load balancing, proxies, ports, TLS, and service connectivity. Proficiency in Python, Shell, or another language used to build infrastructure automation and operational tooling. Experience with monitoring, logging, alerting, dashboards, and incident investigation. Strong debugging and problem-solving skills, including the ability to investigate issues spanning applications, infrastructure, networking, and operating systems. Preferred Skills & Qualifications Experience with infrastructure-as-code tools, specifically Terraform. Experience with Kubernetes controllers, operators, custom resources, Helm, Argo CD, or similar platform technologies. Experience managing artifact repositories, package registries, build caches, or software-distribution infrastructure. Familiarity with build systems, dependency management, and reproducible-build practices. Experience supporting hybrid environments spanning cloud infrastructure, on-premises systems, and specialized hardware. Experience with identity and access management, secrets, certificates, TLS, or mTLS. Experience building internal developer platforms or self-service infrastructure products. BS/MS in Computer Science or a related field, or equivalent practical experience. 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: 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. Find out more about what it's like to work at Cerebras here ! 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.