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/
Hybrid
Design, implement, and optimize state-of-the-art transformer architectures for NLP and computer vision on Cerebras hardware. Collaborate across software, hardware, and product teams to research and prototype novel inference algorithms that exploit unique hardware capabilities.
Hybrid · Toronto
Translate complex cluster architectures into precise, deployment-ready infrastructure designs including rack layouts, power, and cabling. Collaborate with cross-functional teams to validate system integration and automate design workflows for scalable deployments.
On-site
The role focuses on improving the reliability of compute clusters and internal production services through kernel-centric debugging and failure analysis. Responsibilities include collaborating with hardware and software teams to co-design next-generation architectures and participating in incident response.
Hybrid · Toronto
You will be responsible for building and maintaining the software infrastructure that manages large-scale clusters of servers and switches. This includes developing Kubernetes operators, automating bare-metal networking, and ensuring the health and observability of the fleet.
On-site
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.
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 · 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.
On-site · Vancouver
Lead infrastructure operations and deployment execution across Western Canada to ensure AI infrastructure meets uptime and reliability targets. Act as the primary interface with facility providers and manage local teams to execute HQ-driven requirements.
On-site
Design, build, and operate network security controls across data centers, corporate infrastructure, and AWS cloud environments. Own the lifecycle of firewalls and segmentation while automating security infrastructure as code.
On-site
Lead the architectural design and roadmap for enterprise, data center, and cloud networks to support high-performance AI inference. Partner with security and infrastructure teams to implement zero-trust designs and mentor engineers across global regions.
On-site
Lead the design and implementation of security controls for AI platforms, training and inference workflows, and agentic systems. Develop reusable security patterns and automate policy validation to protect sensitive data and customer environments.
On-site · Toronto
Develop and maintain infrastructure to build, test, and operate the Cerebras software stack. Design scalable workflows for automating processes in the cloud and datacenters to monitor software performance on the Wafer Scale Engine.
Hybrid · Toronto
Collaborate with engineers to design, implement, and debug software solutions for next-generation AI infrastructure. Focus on low-level software components, system performance, and reliability across a fully integrated AI-accelerated platform.
