Cerebras Systems is a maker of high-performance AI infrastructure and the Wafer-Scale Engine (WSE) family of AI processors.
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 https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. ABOUT THE ROLE We are looking for an experienced Senior Technical Program Manager to create one accountable operating layer across feature delivery, release integration testing, Core Infrastructure, branch stability, and release readiness within AI Inference Core. The operating mechanism for Inference Core — making complex technical execution explicit, measurable, and consistently followed. You will ensure that cross-team initiatives have clear ownership, documented entry and exit criteria, visible health and SLA tracking, timely escalation, and dependable follow-through. You will turn priorities into an integrated execution model without taking technical ownership away from engineering. This is not a project-tracking or meeting-coordination role. You will need enough technical depth to understand complex AI systems, challenge unclear plans, identify hidden dependencies, improve decision quality, and create operating mechanisms that engineering teams trust and use. WHAT MAKES THIS ROLE DISTINCT - End-to-end operating ownership: Connect feature development, qualification, release testing, infrastructure readiness, branch health, release qualification, and deployment. - Mechanism—not technical outcome: Own process design, planning cadence, dashboards, SLA reporting, dependency tracking, decision logs, and escalation follow-through; engineering retains architecture and quality decisions. - Feature-flow clarity: Make owners, entry and exit criteria, evidence, dependencies, and exception paths explicit as work moves toward release. - Stability accountability: Create visibility and follow-through for pre-merge and post-merge E2E health and SLA breaches. - Cross-team leverage: Coordinate roadmaps, staffing, risks, and ownership boundaries across multiple engineering functions and partner teams. - Leadership leverage: Reduce coordination load on engineering leads so they can focus on architecture, technical strategy, infrastructure, and integration quality. WHAT YOU WILL DO - Define how features move from development and feature qualification into release integration testing and release qualification, including owners, entry and exit criteria, required evidence, dependencies, and exception paths. - Own the operating mechanism for pre-merge and post-merge E2E stability: publish health, track SLA breaches, drive triage and escalation, document decisions, and close recurring-failure loops. - Convert Inference Core priorities into clear goals, milestones, owners, risks, success measures, and review cadences while maintaining one dependable view of commitments. - Coordinate planning and execution across release integration testing, Core Infrastructure, feature teams, release owners, and partner organizations; synchronize roadmaps, staffing, and cross-team dependencies. - Build and maintain dashboards, SLA reporting, dependency maps, risk registers, decision logs, action tracking, and executive-ready status communication. - Drive timely decisions and follow-through on blocked or slipping work, surfacing trade-offs and escalating when teams cannot resolve issues at the working level. - Continuously improve the operating model using delivery data, retrospectives, recurring-failure patterns, stakeholder feedback, and changes in business priorities. MINIMUM SKILLS & QUALIFICATIONS - Significant experience leading complex technical programs across multiple engineering teams, ideally in infrastructure, distributed systems, platforms, release engineering, or AI systems. - Strong technical fluency and the ability to understand architecture, system dependencies, quality evidence, operational risk, and engineering trade-offs. - Proven ability to design and implement durable operating mechanisms, not merely report status or schedule meetings. - Experience defining goals, milestones, ownership, entry and exit criteria, SLAs, risk management, and executive review cadences. - Ability to influence technical leaders and teams without direct authority while preserving clear engineering ownership. - Strong analytical skills and experience using metrics, dashboards, and qualitative evidence to improve execution and decision quality. - Exceptional written and verbal communication, including concise executive synthesis and clear escalation under ambiguity or pressure. PREFERRED SKILLS - Experience with AI infrastructure, model delivery, high-performance computing, distributed systems, or hardware/software platforms. - Experience supporting feature integration, release qualification, branch stability, developer infrastructure, or production readiness. - Familiarity with software quality, E2E testing, CI/CD, observability, incident learning, and reliability mechanisms. - Experience coordinating teams with distinct but overlapping ownership boundaries. - Experience in a startup or similarly fast-moving, resource-constrained engineering environment. - Track record of taking an operating model or cross-team program from zero to one and scaling it as the organization grows. - Technical or engineering background sufficient to build credibility with senior engineers and technical leaders. WHAT SUCCESS LOOKS LIKE - Adoption: