Member of Technical Staff (Software Engineer)

Cerebras Systems•Sunnyvale, CA

23h•Remote

About The Position

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)

Requirements

Master’s degree or foreign equivalent degree in Computer Science, or a related field and 1 year of experience as Software Developer, Student/Intern (Software Developer), Member of Technical Staff (Software Engineer), Software Engineer, or a related occupation required.
Docker and Kubernetes
Java or C++
ActiveMQ and Kafka
Python or Groovy
JavaScript or TypeScript
Linux
SQL, OracleDB, and Redis
Git

Responsibilities

Implement infrastructure to support high-performance, low-latency inference service.
Deploy and configure Kubernetes services to ensure scalability and reliability of inference workloads.
Optimize resource allocation and auto-scaling policies to handle variable inference demand while minimizing operational costs.
Integrate inference services with containerized environments using Docker and Kubernetes for orchestration.
Ensure high availability and fault tolerance by implementing multi-region deployments and disaster recovery strategies.
Develop Python-based scripts and APIs to streamline data preprocessing, inference execution, 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.
Author detailed technical documentation for infrastructure 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.