Search the 37 jobs at Cerebras Systems

As a software engineer on the AI cloud platform, you will optimize deployment for low latency and efficient load balancing while ensuring the reliability and scalability of the distributed infrastructure. You'll develop tools to identify system bottlenecks and enhance API server management.

As a Senior or Staff Product Manager, you will lead the strategy and execution for a cluster management solution that enhances performance and security for AI clusters, ensuring simplified operations for complex infrastructure while integrating seamlessly into customer workflows.

The Operations Financial Analyst analyzes and reports on financial data related to company operations, collaborates with finance and operations teams, manages budgets and forecasts, identifies cost-saving opportunities, and communicates financial insights to stakeholders.

As Principal Product Manager for ML Training at Cerebras Systems, you will design and lead the development of the ML training ecosystem, ensuring a seamless interface for researchers to preprocess, train, fine-tune, and evaluate large ML models on Cerebras chips. You'll drive the product roadmap for CSTorch and Model Zoo, collaborate with technical teams, and focus on reducing the time-to-insight for ML research.

As a Distributed Software Engineer, you will automate configuration, develop orchestration systems, and ensure robust monitoring for large-scale AI Supercomputers in a cluster environment. This role involves working with various software and tools to minimize downtime and enhance resource management in both on-premise and cloud deployments.

As a hardware-focused Data Engineer at Cerebras, you will develop and maintain reliable data processing pipelines, analyze and manage large volumes of data, and collaborate with engineering teams to drive data-driven decision-making that enhances hardware manufacturing processes.

Develop and maintain system administration and monitoring services. Collaborate on defining requirements and integrate solutions across software and hardware domains. Write quality, testable code, and optimize database systems. Proficiency in Go and C/C++ is essential.

You will be responsible for silicon testing, repair, and burn-in of Cerebras wafer scale engines. Key tasks include collaborating on silicon testing, utilizing redundancy for repairs, running algorithms to optimize wafer performance, debugging production issues, and recommending solutions based on root cause analysis.

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.

The Quality Engineer Lead at Cerebras is responsible for standardizing quality metrics across the system pipeline, prioritizing and driving quality improvements, collaborating with systems engineering, and implementing quality control processes. This role requires strong analytical skills, leadership, and the ability to work under pressure to ensure high-quality standards are maintained throughout the product lifecycle.

The Senior Hardware Technical Program Manager at Cerebras Systems leads the operational excellence of high-performance AI compute systems, managing end-to-end hardware schedules, engineering issues, and BOM. The role involves collaboration across multiple teams to ensure efficient deployment of supercomputers, technical risk mitigation, and cost management.

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 Principal Product Manager for ML at Cerebras will be responsible for productizing key ML use cases by collaborating with product leadership, research teams, and engineering. This role involves defining product roadmaps, understanding market requirements, and communicating effectively across various audiences to drive AI innovations.

As a Physical Design Engineer, you will be involved in the synthesis, place, and route of critical processing elements, with a focus on physical design and implementation. You'll collaborate with the RTL team and ensure full-chip integration while optimizing performance, power, and area.

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.

Cerebras is seeking a Performance Technical Lead Manager to build performance models, optimize kernel microcode and compiler algorithms for ML model utilization, debug runtime performance, design features for ML architectures, and develop performance visualization tools. The role requires strong leadership and communication skills, with a focus on computer architecture and performance validation.

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.