Careers at Cerebras

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.

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 We are looking for a hands-on SDET Technical Lead to establish and lead Release Integration Testing within Release & Feature Qualification for AI Inference Core. The Production Engine for Inference Core — turning integrated features into reliable production releases. You will define the quality strategy across the pre-release and release cycle, from feature and model integration through branch stability, release qualification, deployment, and post-release learning. You will work across AI frameworks, runtime, compiler, kernels, distributed systems, infrastructure, and hardware to make release risk visible and actionable. This is a technical-leadership role, not a coordination-only position. You will design test architecture, lead difficult debugging and release decisions, mentor engineers, and write software and automation alongside the team. Release Integration Testing (RIT) is the bridge between feature qualification and release qualification. Feature teams retain ownership of feature design, feature-level qualification, and feature regression. Release Integration Testing owns inference-core integration strategy, inference-path readiness approval, integrated cross-stack validation, and first-pass rollout triage. What Makes This Role Distinct Dedicated Release Integration Testing ownership: Engage before feature qualification completes while keeping the boundary clear: feature teams own feature behavior and qualification; Release Integration Testing owns integration strategy, readiness approval, integrated validation, and first-pass rollout triage. Inference-path readiness gate: Require evidence across unit, simulation, benchmark, feature, and integration testing, with explicit coverage gaps before release entry. Cross-stack test strategy: Define risk-based E2E and regression coverage for features spanning components, organizations, software layers, infrastructure, and hardware. Branch and rollout leadership: Establish measurable health standards for master and release branches, and coordinate inference-impacting rollout across multiple product and release projects. Hands-on technical authority: Lead through code, test architecture, difficult debugging, quality metrics, and evidence-based release decisions. Team multiplier: Raise the technical bar, mentor engineers, and align feature, infrastructure, integration, qualification, and release teams. What You Will Do Define the Release Integration Testing strategy, engagement criteria, ownership boundaries, entry and exit criteria, coverage expectations, and escalation thresholds for AI Inference Core. Engage early on high-risk inference changes; identify dependencies and interaction risks across runtime, host, device programming, memory, scheduling, model execution, infrastructure, and hardware. Own the inference-path readiness gate by reviewing unit, simulation, benchmark, feature-test, and integration evidence, documenting gaps, and approving integration readiness before release entry. Lead integrated inference E2E validation across features and the cloud-to-wafer stack; promote durable feature tests and add risk-based scenarios to release regression. Improve master and release-branch stability through actionable health metrics, failure classification, release-quality reporting, dashboards, qualification workflows, and release pipelines. Lead first-pass regression and rollout triage, coordinate owners through resolution, drive RCA, place missing coverage at the correct layer, and plan rollout across multiple product and release projects. Partner with and mentor SDETs, feature teams, Integration, Core Infra, release owners, and deployment teams; between active engagements, advance automation efficiency, diagnostics, probes, and roadmap test planning. Minimum Skills & Qualifications Strong software-engineering fundamentals and programming ability in Python Go, or a similar language. Demonstrated technical leadership in software quality, test infrastructure, systems validation, release engineering, or complex software integration. Experience designing automation and test architecture for distributed, systems-level, infrastructure, or AI software. Proven ability to break down ambiguous cross-stack failures, form hypotheses, gather evidence, and drive issues to resolution. Strong understanding of risk-based testing, release readiness, regression strategy, failure analysis, and quality metrics. Ability to influence and align multiple engineering teams without relying solely on organizational authority. Clear communication and sound judgment during high-pressure release situations, including the ability to explain technical risk to engineering and leadership audiences. Preferred Skills Experience with software/hardware co-design, hardware accelerators, compilers, kernels, runtimes, or low-level systems. Experience with AI infrastructure, model deployment, LLMs, multimodal workloads, or large-scale compute clusters. Experience building test frameworks, distributed test systems, release pipelines, dashboards, or internal developer tooling. Experience with performance testing, profiling, observability, fault injection, reliability, or production failure analysis. Experience in a startup or similarly fast-moving, resource-constrained engineering environment. Track record of taking a quality or release capability from zero to one and scaling it across teams. Familiarity with containers, cluster orchestration, cloud infrastructure, CI/CD, or high-performance computing. What Success Looks Like Release readiness is based on explicit criteria and high-signal evidence rather than intuition. Fewer inference-path integration defects are first discovered in final release qualification or production. Cross-component risks are found earlier, debug cycles are shorter, and coverage ownership is explicit. Master and release-branch health is measurable, actionable, and steadily improving. Test automation and release infrastructure shorten feedback loops without sacrificing signal quality. Release metrics and reports drive clear decisions, ownership, and predictable feature rollout. Engineers across the organization are more effective because Release Integration Testing provides strong technical direction, tooling, and mentorship. Location This role requires in-office presence, at least three days per week. Fully remote work is not available. Office locations: Sunnyvale, CA or Toronto, ON. 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 an exceptional PCB Layout Lead to own the physical implementation of complex, high-performance printed circuit boards for AI systems. This role will translate schematics and engineering requirements into production-ready layouts, optimizing signal integrity, power integrity, thermal performance, reliability, and manufacturability. Responsibilities Own multilayer PCB layout from initial component placement through fabrication and assembly release. Translate schematics, mechanical constraints, and electrical requirements into constraint-driven board designs using industry-standard PCB layout tools. Define and implement board stackups, controlled-impedance structures, routing rules, reference planes, return paths, and length- and phase-matching constraints. Place and route high-speed digital, high-current power, and sensitive analog circuits, including dense BGA fanout and differential interfaces. Apply signal-integrity and power-integrity best practices to minimize crosstalk, discontinuities, noise, voltage drop, and electromagnetic interference. Collaborate with electrical, SI/PI, mechanical, thermal, compliance, manufacturing, and test engineers to establish constraints and resolve layout issues. Work with fabrication and assembly partners to select materials, validate stackups and via structures, and improve yield, cost, and schedule. Perform design reviews and DFM, DFA, and DFT checks; generate and verify fabrication drawings, assembly drawings, drill data, Gerbers, ODB++, IPC-2581, and related release packages. Maintain component footprints, padstacks, design libraries, layout standards, and release documentation. Support prototype builds, board bring-up, failure analysis, and layout revisions through production. Minimum Qualifications Bachelor’s degree in electrical engineering, electronics engineering, or a related field, or equivalent practical experience. 10+ years of hands-on experience laying out complex, high-speed, multilayer printed circuit boards. Expertise with Cadence Allegro PCB Designer and Constraint Manager; experience with Altium Designer or equivalent tools is also relevant. Strong knowledge of component placement, controlled-impedance routing, differential pairs, length matching, return-path continuity, and power distribution. Experience with dense BGA escape routing, HDI structures, blind and buried vias, microvias, via-in-pad, and backdrilling. Working knowledge of signal integrity, power integrity, EMI/EMC, thermal design, creepage and clearance, and high-current routing. Experience applying IPC standards and fabrication, assembly, test, and manufacturability requirements. Ability to create and review complete PCB fabrication and assembly release packages. Ability to thrive in a fast-paced, dynamic environment and adapt to changing priorities. Excellent collaborative and interpersonal skills with multi-disciplined teams. Experience working on server hardware, AI accelerators, hardware accelerator, datacenter, AI Hardware, enterprise product, GPU, CPU, TPU, server platform etc. Preferred Qualifications Experience designing large, high-layer-count boards for compute, networking, server, or AI accelerator systems. Experience routing high-speed interfaces such as DDR4/DDR5, PCIe Gen4/Gen5 or later, Ethernet, USB, and multi-gigabit SerDes. Experience with high-current, low-voltage power delivery, VRM placement, decoupling strategy, copper balancing, and thermal optimization. Experience developing Allegro SKILL, scripts, or automation that improves layout quality and release efficiency. Demonstrated success delivering first-pass functional boards and supporting products from prototype through volume production. Location: Remote. Sunnyvale, CA (Hybrid) is highly preferred. The base salary range for this position is $210,000 – $230,000+ annually. Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications. 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 an exceptional PCB Layout Lead to own the physical implementation of complex, high-performance printed circuit boards for AI systems. This role will translate schematics and engineering requirements into production-ready layouts, optimizing signal integrity, power integrity, thermal performance, reliability, and manufacturability. Responsibilities Own multilayer PCB layout from initial component placement through fabrication and assembly release. Translate schematics, mechanical constraints, and electrical requirements into constraint-driven board designs using industry-standard PCB layout tools. Define and implement board stackups, controlled-impedance structures, routing rules, reference planes, return paths, and length- and phase-matching constraints. Place and route high-speed digital, high-current power, and sensitive analog circuits, including dense BGA fanout and differential interfaces. Apply signal-integrity and power-integrity best practices to minimize crosstalk, discontinuities, noise, voltage drop, and electromagnetic interference. Collaborate with electrical, SI/PI, mechanical, thermal, compliance, manufacturing, and test engineers to establish constraints and resolve layout issues. Work with fabrication and assembly partners to select materials, validate stackups and via structures, and improve yield, cost, and schedule. Perform design reviews and DFM, DFA, and DFT checks; generate and verify fabrication drawings, assembly drawings, drill data, Gerbers, ODB++, IPC-2581, and related release packages. Maintain component footprints, padstacks, design libraries, layout standards, and release documentation. Support prototype builds, board bring-up, failure analysis, and layout revisions through production. Minimum Qualifications Bachelor’s degree in electrical engineering, electronics engineering, or a related field, or equivalent practical experience. 10+ years of hands-on experience laying out complex, high-speed, multilayer printed circuit boards. Expertise with Cadence Allegro PCB Designer and Constraint Manager; experience with Altium Designer or equivalent tools is also relevant. Strong knowledge of component placement, controlled-impedance routing, differential pairs, length matching, return-path continuity, and power distribution. Experience with dense BGA escape routing, HDI structures, blind and buried vias, microvias, via-in-pad, and backdrilling. Working knowledge of signal integrity, power integrity, EMI/EMC, thermal design, creepage and clearance, and high-current routing. Experience applying IPC standards and fabrication, assembly, test, and manufacturability requirements. Ability to create and review complete PCB fabrication and assembly release packages. Ability to thrive in a fast-paced, dynamic environment and adapt to changing priorities. Excellent collaborative and interpersonal skills with multi-disciplined teams. Experience working on server hardware, AI accelerators, hardware accelerator, datacenter, AI Hardware, enterprise product, GPU, CPU, TPU, server platform etc. Preferred Qualifications Experience designing large, high-layer-count boards for compute, networking, server, or AI accelerator systems. Experience routing high-speed interfaces such as DDR4/DDR5, PCIe Gen4/Gen5 or later, Ethernet, USB, and multi-gigabit SerDes. Experience with high-current, low-voltage power delivery, VRM placement, decoupling strategy, copper balancing, and thermal optimization. Experience developing Allegro SKILL, scripts, or automation that improves layout quality and release efficiency. Demonstrated success delivering first-pass functional boards and supporting products from prototype through volume production. Location: Remote. Sunnyvale, CA (Hybrid) is highly preferred. The base salary range for this position is $210,000 – $230,000+ annually. Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications. 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. We are looking for an Application Security Engineer with a strong focus on Vulnerability Management to help secure Cerebras software, infrastructure, and AI platforms. You will own and evolve key parts of our vulnerability management program from vulnerability discovery and risk prioritization through remediation and verification. This is not a ticket-management role. We are looking for an engineer who can understand vulnerabilities in context, work directly with engineering teams, automate repetitive security work, and help eliminate classes of vulnerabilities rather than simply track individual findings. You will work closely with Engineering, Infrastructure, and IT teams to identify vulnerabilities across our products and environments and drive them to meaningful remediation. Responsibilities Own and continuously improve vulnerability management across applications, software dependencies, containers, operating systems, cloud infrastructure, and externally exposed services. Use AI agents and advanced security models to augment vulnerability discovery, code analysis, adversarial testing, prioritization, and remediation. Analyze vulnerabilities beyond scanner severity by considering exploitability, exposure, affected assets, available mitigations, and business impact. Partner directly with engineering teams to triage findings, determine appropriate remediation, and drive vulnerabilities to closure within risk-based SLAs. Build automation for vulnerability ingestion, deduplication, enrichment, prioritization, assignment, remediation tracking, and reporting. Identify systemic vulnerability patterns and work with engineering teams to address root causes rather than repeatedly fixing individual findings. Operate and improve application security capabilities including SAST, SCA, secrets detection, container scanning, infrastructure scanning, and external attack-surface monitoring. Validate security findings through technical investigation and hands-on testing, distinguishing exploitable vulnerabilities from false positives and low-risk findings. Perform targeted application security reviews and testing for high-risk services and features. Help integrate security controls into CI/CD and developer workflows while minimizing unnecessary friction for engineering teams. Develop metrics and reporting that provide meaningful visibility into vulnerability exposure, remediation performance, recurring vulnerability classes, and security risk. Evaluate and integrate new security technologies as our software and infrastructure environments evolve. Partner with security and engineering teams on incident response when vulnerabilities are actively exploited or require urgent remediation. Skills & Qualifications We are looking for candidates who: Have strong application security or product security fundamentals and hands-on experience with vulnerability management. Understand common vulnerability classes and exploitation techniques across web applications, APIs, authentication systems, cloud services, containers, and software supply chains. Can read and reason about code and work effectively with software engineers on practical remediation. Have experience with vulnerability scanning and application security technologies such as SAST, SCA, DAST, secrets scanning, container scanning, or CSPM. Understand vulnerability prioritization concepts including CVSS, exploitability, asset criticality, internet exposure, compensating controls, and threat intelligence. Are comfortable investigating security findings manually rather than relying exclusively on scanner output. Can automate security workflows using languages such as Python, Go, or similar scripting/programming languages. Have experience integrating security tooling into CI/CD and modern software development workflows. Are comfortable working with Linux, Git, containers, and cloud environments. Can communicate security risk clearly to both security specialists and engineering teams. Approach security with an automation-first mindset and look for scalable solutions instead of manual processes. Preferred Qualifications Experience in one or more of the following areas is a plus: Application Security or Security engineering background. Securing AI/ML platforms, inference services, or large-scale compute infrastructure. Building or operating vulnerability management programs at scale. AWS, Kubernetes, Docker, and cloud-native environments. Software supply-chain security and dependency management. GitHub and CI/CD security. Threat modeling and secure design reviews. Penetration testing or offensive security. External attack-surface management. Vulnerability research or exploit validation. Security data pipelines, APIs, and workflow automation. Using LLMs, AI agents, or AI-assisted security tooling for vulnerability research, code analysis, penetration testing, or remediation. What Success Looks Like You will help Cerebras move beyond simply finding vulnerabilities toward an engineering-driven vulnerability management program where: The vulnerabilities that create the most meaningful risk are identified and prioritized quickly. Engineering teams receive actionable findings with clear remediation guidance. High-risk vulnerabilities are consistently remediated within defined SLAs. Security findings are increasingly validated, prioritized, and routed automatically. Recurring vulnerability classes are addressed systematically. AI and automation reduce manual security work and accelerate both vulnerability discovery and remediation. Most importantly, you will help build security processes that scale with the speed at which Cerebras builds and deploys some of the most advanced AI systems in the world.ADD DESCRIPTION HERE 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. Responsibilities: Drive the design and architecture of secure, scalable cloud infrastructure and identity platforms in AWS and our own data centers. Design and implement IAM, IGA, authentication, authorization, SSO, MFA, identity lifecycle management, and provisioning/deprovisioning solutions using modern identity platforms and standards such as SAML, OAuth2, OIDC, and SCIM. Develop automation, integrations, and infrastructure-as-code solutions using Terraform and programming languages such as Python and Go. Design and implement security controls for AI-powered systems, including controlled, audited, and governed agent workflows, while contributing to core security services such as service identity, secrets management, key management, authentication, and authorization. Partner across Security, Engineering, and Infrastructure teams to drive secure-by-design solutions, implement Zero Trust principles, and reduce operational friction. Drive technical direction and cross-team initiatives that improve the scalability, reliability, security, and developer experience of our infrastructure. Write high-quality, reliable code, participate in architecture and code reviews, mentor engineers, and help raise the technical bar across the team. Support critical production systems and drive operational excellence through scalability, resiliency, observability, and automation. Participate in on-call and incident response for the Developer Productivity organization. Skills & Qualifications 7+ years of experience in Cloud Infrastructure, Platform Engineering, Identity & Access Management, Identity Engineering, or Security Engineering. Proven experience designing and owning complex production infrastructure or platform systems at scale. Strong knowledge of authentication, authorization, identity lifecycle management, and federation protocols including SAML, OAuth2, OIDC, SCIM, and RBAC. Experience designing and operating identity and access controls within AWS environments, with hands-on experience building and operating production-level services on AWS. Experience working with container technologies and deploying and operating services on Kubernetes. Strong automation and coding skills with Python or Go, along with Terraform or similar IaC technologies. A security-first mindset with experience implementing Zero Trust, least-privilege access, and compliance frameworks such as SOC2, FedRAMP, or ITAR. A strong platform and operational mindset, with experience supporting and improving production services through monitoring, troubleshooting, incident response, and automation. Demonstrated ability to influence technical direction, drive cross-team initiatives, and mentor other engineers. Excellent collaboration and communication skills, with a proven ability to work across teams and influence technical decisions. Preferred Skills & Qualifications Hands-on experience with identity platforms such as Okta, Microsoft Entra ID (Azure AD), or modern IAM/IGA platforms. Experience with Azure and/or GCP is a bonus. 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.