DevOps Architect

<div class="content-intro"><p>Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities.</p></div><p data-start="181" data-end="444">Tenstorrent is building multi-megawatt AI data centers with thousands of accelerators. We are seeking a DevOps Architect to define the next generation cluster control plane that provisions, operates, and secures large-scale AI training and inference environments.</p> <p data-start="446" data-end="579">This is a foundational architecture role. You will define how clusters are configured, orchestrated, monitored, and secured at scale.</p> <p>This role is<strong> </strong>hybrid, based out of Austin, TX; Santa Clara, CA; or Toronto, ON.</p> <p>We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.</p> <p> </p> <p><strong>Who You Are</strong></p> <ul> <li data-start="648" data-end="742">10+ years designing and operating enterprise, HPC, or large-scale data center infrastructure</li> <li data-start="648" data-end="742">Deep expertise in cloud-native and bare-metal infrastructure management</li> <li data-start="648" data-end="742">Strong hands-on experience with Infrastructure-as-Code tools such as Terraform, Ansible, and Helm</li> <li data-start="648" data-end="742">Experienced building and operating observability stacks including Prometheus, Grafana, ELK or EFK, and OpenTelemetry</li> <li data-start="648" data-end="742">Strong understanding of networking, storage systems, accelerator resource management, and security models including RBAC, IAM, TLS, and secrets management</li> </ul> <p> </p> <p><strong>What We Need</strong></p> <ul> <li data-start="1226" data-end="1371">Define the end-to-end architecture for the AI cluster control plane, covering provisioning, configuration, lifecycle management, and monitoring</li> <li data-start="1226" data-end="1371">Architect scalable systems for system, network, and storage provisioning across multi-thousand accelerator environments</li> <li data-start="1226" data-end="1371">Establish telemetry, logging, metrics, tracing, and alerting frameworks with operational guardrails</li> <li data-start="1226" data-end="1371">Define workload placement, resource allocation, scheduling, and preemption policies to maximize accelerator utilization</li> <li data-start="1226" data-end="1371">Integrate authentication, authorization, account management, key management, backup, checkpointing, and DCIM infrastructure into a secure multi-tenant environment</li> </ul> <p> </p> <p><strong>What You Will Learn</strong></p> <ul> <li data-start="1923" data-end="1997">How multi-megawatt AI data centers are architected and operated at scale</li> <li data-start="1923" data-end="1997">The operational challenges of orchestrating thousands of AI accelerators during training and inference</li> <li data-start="1923" data-end="1997">How distributed infrastructure decisions directly impact AI model throughput, reliability, and cost efficiency</li> <li data-start="1923" data-end="1997">Advanced strategies for multi-tenant isolation, security hardening, and workload optimization in AI clusters</li> <li data-start="1923" data-end="1997">How hardware, runtime systems, and control plane architecture co-evolve in next-generation AI infrastructure</li> </ul> <p> </p ... (truncated, view full listing at source)