Sr. Member of Technical Staff

Cerebras Systems•Sunnyvale, CA

20h•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 Sr. Member of Technical Staff.

Requirements

Master’s degree or foreign equivalent degree in Computer Science, or a related field and 18 months of experience as Information Security Analyst, Software Engineer, Sr. Member of Technical Staff, IT Senior Applications Engineer, or a related occupation required.
18 months of experience with Infrastructure-as-Code and deployment automation: Terraform, AWS CloudFormation, AWS CDK, and Ansible
18 months of experience with Containerization and orchestration: Docker, Kubernetes, AWS EKS, AWS Elastic Container Service (ECS), AWS Fargate, and Helm
18 months of experience with Compute and serverless services : AWS EC2, AWS Lambda functions, and Auto Scaling Groups
18 months of experience with Monitoring, logging, and distributed tracing : AWS CloudWatch, AWS X-Ray, ELK (Elasticsearch, Logstash, Kibana), Prometheus, and Grafana
18 months of experience with Programming languages and frameworks : Python, Node.js, JavaScript, and Flask
18 months of experience with Data storage and caching : PostgreSQL, Redis, and NFS
18 months of experience with CI/CD and version control : Jenkins and Git

Responsibilities

Design and develop software features that support system resiliency and high availability, including automated recovery mechanisms and fault-tolerant architecture across distributed environments.
Develop and maintain cloud-based deployment workflows for AI inference software using AWS tools and services to support low-latency and scalable system performance.
Develop Python-based scripts and APIs to streamline data preprocessing, inference execution, and post-processing for real-time inference tasks.
Use parallel programming techniques (e.g., multi-threading, asynchronous processing) to maximize resource efficiency on AWS compute instances.
Develop software components to support visualization and analysis of system performance metrics, enhancing the monitoring and usability of inference services.
Develop inference software in Docker containers and define Kubernetes orchestration strategies that ensure software reliability and efficient scaling.
Develop automated scripts to detect and mitigate common failure modes, improving software system reliability.
Debug issues related to model deployment, container orchestration, networking configurations, documenting steps to reproduce and root-cause defects.
Triage and resolve defects in the software service by analyzing logs, metrics, and distributed traces using tools like AWS CloudWatch, Grafana, or custom Python scripts.
Work with product management and user experience teams to define requirements for inference service interfaces, including configuration, monitoring, and event logging.
Author detailed technical documentation for infrastructure configurations, inference workflows, and APIs, ensuring clarity for internal teams and external customers.
Document and track defects, enhancements, and release notes using tools like Jira and Git, ensuring version control and traceability.