Sr. Member of Technical Staff

Cerebras Systems; United States; Remote; USD 180,000 - 250,000; Full time

Job summary

Cerebras Systems seeks a Sr. Member of Technical Staff to design and develop software features that improve resiliency and availability across distributed environments.

You will help build scalable AI inference services, craft cloud deployment workflows on AWS, and automate reliability through code and testing. You will collaborate with engineering teams to deliver high-performance software, implement Docker/Kubernetes deployments, and contribute to comprehensive monitoring and logging

Qualifications

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

Responsibilities

• Design and develop features for resiliency and high availability across distributed environments.
• Develop and maintain cloud-based deployment workflows for AI inference software using AWS for low latency and scalability.
• Develop Python scripts and APIs to streamline data preprocessing, inference, and post-processing for real-time tasks.
• Use parallel programming (multi-threading, async) to maximize resource use on AWS compute.
• Develop components to visualize and analyze system performance metrics (monitoring UI).
• Develop inference software in Docker and define Kubernetes orchestration strategies for reliability and scaling.
• Create automated scripts to detect and mitigate common failure modes to improve reliability.
• Debug issues related to model deployment, containers, networking; document steps and root causes.
• Triage and resolve defects by analyzing logs, metrics, and traces using AWS CloudWatch, Grafana, etc.
• Collaborate with Product and UX to define requirements for inference service interfaces (config, monitoring, logging).
• Document infrastructure configurations, inference workflows, and APIs for internal and external customers.
• Track defects, enhancements, and release notes using Jira and Git.

Skills

Terraform

AWS CloudFormation

Kubernetes

Docker

Python

Node.js

JavaScript

Flask

PostgreSQL

Redis

Jenkins

Git

Grafana

Prometheus

ELK

Education

Master’s degree in Computer Science or related field

Tools

Grafana

Prometheus

ELK Stack

Jenkins

Git

Job description

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.

About the Role

We are seeking a Sr. Member of Technical Staff to design and develop software features that support system resiliency and high availability across distributed environments. In this role, you will help build and maintain scalable AI inference services, develop cloud-based deployment workflows, improve system reliability through automation, and collaborate across engineering teams to deliver high-performance software solutions.

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, and 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.

Skills & Qualifications

Minimum Requirements

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

Why Join Cerebras

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 business. Members of our team tell us there are five main reasons they joined Cerebras:

1. Build a breakthrough AI platform beyond the constraints of the GPU.
2. Publish and open source their cutting-edge AI research.
3. Work on one of the fastest AI supercomputers in the world.
4. Enjoy job stability with startup vitality.
5. Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it’s like to work at Cerebras here!

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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