This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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.
Job Responsibility:
Lead Complex Programs: Own delivery of large, cross-functional initiatives across Cerebras’ AI training and inference platforms
Align Stakeholders: Partner with engineering, product, hardware, and infrastructure teams to define scope, priorities, and timelines
Plan & Execute: Turn ambiguous goals into clear roadmaps, milestones, and measurable outcomes
Engage Technically: Participate in system design and architecture discussions to drive informed tradeoffs
Manage Risks & Dependencies: Surface blockers early, coordinate across teams, and resolve escalations quickly
Improve Execution: Establish strong planning cadences, tracking, and metrics to increase predictability and velocity
Scale Impact: Refine processes and share best practices to help teams operate more effectively
Requirements:
B.S./M.S. in Computer Science, Electrical Engineering, or related technical field (or equivalent experience)
7–10+ years leading complex technical programs or product delivery
Experience delivering complex technical programs across software platforms, distributed systems, or infrastructure
Strong technical understanding of AI/ML systems, including training and inference workflows
Prior product or platform experience building AI training, inference, or large-scale compute systems strongly preferred
Comfortable working closely with engineers and engaging in technical tradeoff discussions
Excellent prioritization, risk management, and cross-functional alignment skills
Ability to operate independently in fast-paced, ambiguous environments
Experience collaborating with distributed or remote teams
Nice to have:
Experience in productizing AI/ML platform, infrastructure or distributed system solutions (training, serving, compilers, runtimes, or large-scale compute) into reliable, customer-facing solutions
Familiarity with hardware–software co-design or accelerator-based systems
Experience driving 0→1 or ambiguous platform initiatives
Strong grasp of performance, scaling, and reliability metrics
Exposure to customer-facing or field deployments
Experience mentoring TPMs and improving planning/execution processes
Comfortable working across distributed, cross-functional teams
What we offer:
Build a breakthrough AI platform beyond the constraints of the GPU
Publish and open source their cutting-edge AI research
Work on one of the fastest AI supercomputers in the world
Enjoy job stability with startup vitality
Our simple, non-corporate work culture that respects individual beliefs