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Cerebras Systems is seeking a Distributed Systems Security Engineer to strengthen security controls across our on-prem and cloud clusters. You will drive hardening efforts, model threats, and collaborate with platform, infrastructure, and hardware teams to deliver secure, scalable architectures.
The role demands deep hands-on engineering experience in heterogeneous compute environments, extensive Kubernetes hardening, and clear communication to both technical and non-technical stakeholders.
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 with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.Cerebras is seeking a Distributed Systems Security Engineer to help build out the next generation of security controls and services in our on-prem and cloud clusters.In this role you will be responsible for driving hardening efforts across every layer of the cluster stack, covering a range of systems at the bare metal, virtualized, and k8s containerized levels.The ideal candidate brings a combination of wide breadth of knowledge on a variety of systems and networking topics, a pragmatic and results-driven outlook on security, and the flexibility and attention to detail to work in a fast-paced development environment without compromising our security standards.
People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our