Search the 13 jobs at Cerebras Systems

The Senior Product Manager for the Cloud Console will define and execute the product strategy for the administrative web application that manages inference and training services. Responsibilities include conducting user research, translating user requirements into specifications, collaborating with cross-functional teams, and communicating product updates to stakeholders.

As a Senior ML Quality Engineer, you will ensure the quality of software across various ML workloads and workflows by developing testing practices, validating model training accuracy, and debugging components. You will automate workflows and contribute significantly to product quality within a fast-paced environment.

The Senior ML Integration and Ops Engineer will drive technical projects for large-scale LLM model training, focusing on software and hardware integration, automation of workflows, and software specification development. Responsibilities include debugging, effective communication, and enhancing ML product quality, while leading cross-functional efforts and improving integration methodologies.

In this role, the Senior ML Frameworks Engineer will lead a team in integrating machine learning frameworks with Cerebras' advanced software and hardware ecosystem, design APIs for ML models, and optimize systems for high throughput and low latency. The focus will be on collaboration, software quality, and advancing ML solutions.

As a Platform Software Engineer at Cerebras, you'll optimize their AI cloud platform for model training and inference, focusing on latency, load balancing, and system reliability. Responsibilities include defining production requirements, implementing scaling strategies, managing API servers, and resolving system bottlenecks.

The Performance Engineer will build performance models for deep learning applications, optimize kernel microcode and compiler algorithms, debug runtime performance, and design features for ML architectures. Responsibilities also include creating tools for visualizing performance data from the Cerebras WSE and compute clusters.

The ML Stack Optimization Engineer will design, develop, and optimize compiler technologies for AI chips using LLVM and MLIR. They will address performance bottlenecks, work with machine learning teams to integrate optimizations, and contribute to advancing compiler technologies for enhanced performance in AI applications.

The ML Software Tool Development Engineer is responsible for designing and expanding build tools for new hardware and software, resolving issues for ML applications, and collaborating with cross-functional teams to enhance solutions. Candidates should have skills in hardware debugging, CAD tool design, and UI/UX data visualization, with a strong knowledge of C++ and Python.

As an ML Integration and Ops Engineer, you'll orchestrate the integration of software and hardware for LLM model training, drive technical projects, improve workflows, automate testing, and contribute to software specifications for ML products.

The role involves designing and expanding build tool functionality for new hardware and software, debugging ML applications, and collaborating with cross-functional teams to enhance solutions against competitors.

As a Compiler Engineer at Cerebras Systems, you will design and optimize compiler technologies for AI chips using LLVM and MLIR frameworks. You will work closely with the machine learning team to enhance compiler performance, address bottlenecks, and ensure efficient resource utilization for AI applications.

As a Full-Stack Software Engineer, you will design and build user-friendly frontend interfaces for AI cloud applications, ensuring high reliability and efficiency in handling customer traffic. You will utilize web development frameworks and collaborate with teams to enhance the user experience while actively participating in a rapidly evolving environment.

Responsible for deploying and managing clusters in distributed environments, including planning, troubleshooting networking and hardware issues, and maintaining systems for performance and reliability. Collaborate with cross-functional teams to automate and document deployment processes.