Top Tech Jobs & Startup Jobs in Toronto

Operate, monitor, and optimize large-scale AI compute clusters built on the Wafer-Scale Engine. Develop and own software for cluster operations (monitoring, automation, APIs, dashboards), deploy and debug Docker-based services, troubleshoot distributed systems, maximize compute capacity, and participate in a 24/7 on-call rotation while collaborating cross-functionally to improve operational visibility and reliability.

Own quality and reliability of the Cerebras Inference Platform by building test infrastructure and automation, validating Kubernetes and hardware deployments, debugging distributed systems and networking, developing testbeds for performance and scalability, and partnering with platform engineers to ensure production-ready releases.

Harden low-level platform layers across firmware, bootloader, Linux kernel, and hardware roots of trust. Design secure/measured boot and attestation, review kernel modules/drivers for memory safety and privilege issues, build kernel telemetry (eBPF), and drive fleet-wide vulnerability response and remediation.

Lead architecture and evolution of enterprise, data center, and cloud networks for hyperscale AI; design secure, high-performance fabrics (spine/leaf, RDMA); build AI-agent review frameworks; implement segmented zero-trust network designs; set resiliency, observability, and capacity standards; mentor global engineers and guide cross-functional initiatives.

Design, build, and operate firewall, segmentation, and zero-trust controls across data center, corporate, and cloud networks. Implement infrastructure-as-code for policy and CI/CD deployment, manage lifecycle and rule hygiene, build network detection capabilities, operate VPN/remote access patterns, and document architecture and runbooks while partnering with Network Engineering and Security Operations.

Join IT & Security to secure and scale enterprise IT, cloud, network, and infrastructure; build automation and tooling; support detection, response, and vulnerability management; improve identity, endpoint, and systems security; and partner cross-functionally to develop processes that enable secure, reliable, and scalable AI workloads.

Build and maintain customer-facing AI cloud frontends (inference, training, admin consoles, and APIs). Design responsive, high-reliability interfaces to handle high traffic, using modern web frameworks and best practices. Collaborate on architecture, ensure production-grade quality, and thrive in ambiguous, fast-changing environments while sharing knowledge and communicating clearly.

Design, build, and operate CI/CD, Kubernetes-based platforms, deployment automation, and observability for engineering workflows. Improve reliability, performance, and scalability across cloud and on-prem environments, debug cross-boundary failures, perform root-cause analysis, and deliver durable platform software and self-service tooling.

Operate and scale production AI inference infrastructure, run releases and capacity changes, build self-service CD pipelines and automation, extend telemetry and observability, collaborate on SLOs, post-mortems, and capacity planning to reduce operational toil.

Execute hardware bring-up, validation, and telemetry monitoring for Cerebras AI clusters in data centers. Perform power-on sequencing, first-line troubleshooting, log collection, incident support under senior guidance, and contribute feedback to tooling and documentation while learning system architecture and networking fundamentals.