Sr. Staff/Principal SW Security Engineer, AI Inference
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About the Role
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.
About the Role
We’re looking for a Principal/Sr. Staff SW Security Software Engineer to secure large-scale AI inference infrastructure running complex, high-performance workloads. You’ll design and implement security controls across the entire inference stack — from APIs and control planes to distributed systems, accelerators, and runtime environments. This role blends systems engineering, platform security, and operational rigor in an environment where performance and reliability are mission critical. This is a hands-on engineering role with real ownership over production systems.
What You’ll Do
- Architecting, implementing, and scaling robust security solutions for AI inference platforms
- Lead and drive strategic security initiatives, fostering secure by default developer experiences, and mentoring high performing engineering teams.
- Secure model serving, APIs, and control planes against abuse and attacks
- Build authentication, authorization, and identity systems for internal and external services
- Implement network security (service isolation, mTLS, zero-trust patterns)
- Harden inference runtimes, containers, and host systems
- Develop protections against:
- Model extract
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