Member of Technical Staff (Software Engineer)

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. Cerebras Systems Inc. has multiple openings for Member of Technical Staff (Software Engineer) Title: Member of Technical Staff (Software Engineer) Job Duties Implement infrastructure to support high-performance, low-latency inference service. Deploy and co [... source excerpt omitted ...] tion, and post-processing for real-time inference tasks. Collaborate with machine learning engineers to validate inference accuracy and performance against functional and latency requirements. Triage and resolve defects in the service by analyzing logs, metrics, and distributed traces. Debug issues related to model deployment, container orchestration, or networking configurations, documenting steps to reproduce and root-cause defects. Collaborate with cross-functional teams to address performance regressions, scalability issues, or integration failures in the inference pipeline. Develop automated scripts to detect and mitigate common failure modes, improving system reliability. [... source excerpt omitted ...] ucture configurations, inference workflows, and APIs, ensuring clarity for internal teams and external customers. Work with product management and user experience teams to define requirements for inference service interfaces, including configuration, monitoring, and event logging. Document and track defects, enhancements, and release notes using tools like Jira and Git, ensuring version control and traceability. Participate in release planning and prioritization discussions to align infrastructure development with customer needs and business objectives. Minimum Requirements: Master’s degree or foreign equivalent degree in Computer Science, or a related field and 1 year of experi