Cerebras Systems builds the world's fastest AI inference. We are powering the future of generative AI. We’re a team of pioneering computer architects, deep learning researchers, and engineers building a new class of AI supercomputers from the ground up. From sub-second inference speeds to breakthrough training performance, Cerebras makes it easier to build and deploy state-of-the-art AI—from proprietary enterprise models to open-source projects downloaded millions of times. Here’s what makes our platform different: 🔦 Sub-second reasoning – Instant intelligence and real-time responsiveness, even at massive scale ⚡ Blazing-fast inference – Up to 30x faster than GPUs 🧠 Agentic AI in action – Models that can plan, act, and adapt autonomously 🌍 Scalable infrastructure – Built to move from prototype to global deployment without friction Cerebras solutions are available in the Cerebras Cloud or on-prem, serving leading enterprises, research labs, and government agencies worldwide. 👉 Learn more: https://www.cerebras.ai Join us: https://cerebras.net/careers/
On-site · Toronto
Design and maintain CI/CD systems and Kubernetes-based platforms to support engineering workflows. Automate infrastructure changes and perform root-cause analysis to improve system reliability and scalability.
On-site · Toronto
The role involves participating in the bring-up of next-generation AI hardware and debugging complex system-level interactions between hardware and software. The engineer will build automation frameworks and internal tooling to improve system validation, observability, and debugging workflows.
On-site · Toronto
Design and architect secure, scalable cloud infrastructure and identity platforms across AWS and private data centers. Develop automation and security controls for AI-powered systems while implementing Zero Trust principles and mentoring other engineers.
On-site · Toronto
Design and evolve CI/CD pipelines and artifact lifecycle systems to improve build, test, and release workflows. Lead architectural improvements to developer infrastructure and AI tooling to increase engineering productivity and velocity.
On-site · Canada
You will lead the end-to-end technical execution of server and network platform programs, including new product introductions and cluster architecture development. This involves managing vendor engagements, driving technical trade-offs, and ensuring operational readiness across cross-functional teams.
On-site · Toronto
The engineer is responsible for provisioning, commissioning, and validating network infrastructure and servers across large-scale data center deployments. They will develop automation tools and repeatable processes to ensure reliable site readiness and successful handoff to operations teams.
On-site · Toronto
Manage and operate large-scale AI compute clusters using the Wafer-Scale Engine to ensure high availability and performance. Develop software solutions for monitoring, automation, and fleet management to optimize compute capacity.
Hybrid · Toronto
You will design, build, and maintain the GPU inference stack, ensuring high performance and reliability for large-scale AI workloads. Additionally, you will establish operational practices for the GPU fleet, including deployment, monitoring, and performance tuning across distributed systems.
Hybrid · Canada
Establish and lead the Release Integration Testing (RIT) strategy for AI Inference Core to ensure reliable production releases. The role involves designing test architecture, managing the inference-path readiness gate, and leading cross-stack validation from cloud to wafer.
Hybrid · Canada
Design and maintain CI/CD systems and Kubernetes-based platforms to support engineering workflows. Automate infrastructure changes and perform root-cause analysis to improve system reliability and scalability.
Hybrid · Canada
Lead the Inference Model Scaling organization to define the technical vision and roadmap for enabling foundation models on Cerebras hardware. Manage a globally distributed team responsible for ML model compilation, optimization, and high-performance kernel development.
Hybrid · Canada
Integrate, test, and validate the software stack powering the Cerebras AI platform across runtime, compiler, and hardware layers. Develop automated tests and tools to improve the reliability and quality of large-scale machine learning workloads.
Hybrid · Canada
Design and implement high-performance machine learning and linear algebra kernels for the Cerebras Wafer-Scale Engine. Collaborate with hardware and compiler engineers to optimize compute utilization and validate system performance.
Hybrid · Toronto
Innovate and execute automated tests to ensure 99.999% reliability for large-scale AI cluster deployments. Validate cluster software and hardware components including Kubernetes, wafer-scale accelerators, and high-speed interconnects.
On-site · Toronto
Develop and maintain C++ simulator infrastructure for next-generation Wafer-Scale Engine systems, focusing on functional and pipeline-accurate simulation. Collaborate with cross-functional teams to validate architectural behavior and improve internal engineering workflows.
On-site · Toronto
The role involves characterizing and optimizing the performance of AI models on Cerebras hardware to identify bottlenecks and improve efficiency. Responsibilities include building performance models and optimizing kernel microcode to enhance inference speed and throughput.
On-site · Toronto
Design and operate secure, scalable cloud infrastructure and identity platforms across AWS and private data centers. Implement identity lifecycle management and security controls for AI-powered systems using automation and infrastructure-as-code.
On-site · Toronto
Develop and maintain CICD pipelines and artifact lifecycle systems to ensure efficient build and release workflows. Provision and optimize cloud infrastructure while creating internal tooling to enhance developer velocity and engineering productivity.
On-site · Toronto
Design and maintain test infrastructure to validate the Cerebras Inference Platform across cloud and hardware environments. Collaborate with development teams to debug complex issues in networking, orchestration, and distributed services to ensure production readiness.
Hybrid · Toronto
Lead the testing strategy and execution for AI/ML models, focusing on accuracy, fairness, and performance at scale for the ML API features team. Architect end-to-end test strategies and drive automation initiatives to improve engineering efficiency and product quality.
