The Senior Director, Complex Commercial Transactions at Cerebras will lead and negotiate high-value commercial transactions, focusing on hardware revenue deals and significant IP components. The role includes managing agreements, mentoring colleagues, and collaborating with C-suite executives, all within a fast-paced environment. This attorney will develop efficient processes for compliance and negotiation while operating independently.

As an Emulation Engineer at Cerebras, you will develop emulation infrastructure for high-quality pre-silicon verification of Wafer Scale Engines, compile and run testbenches, and create self-checking metrics. You are expected to leverage extensive experience in hardware emulation, design verification, and Python scripting to ensure the performance and accuracy of cutting-edge AI hardware.

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.

As a Kernel Engineer, you will develop specifications and kernel libraries for machine learning and linear algebra applications on Cerebras systems. Responsibilities include debugging, performance measurement, and collaborating with architects to optimize systems. You will also research trends in machine learning to improve kernel library architecture.

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 Runtime Performance Engineer at Cerebras Systems, you will optimize AI applications on x86 architecture, focusing on CPU and memory subsystem enhancements for efficient ML workloads. Responsibilities include developing algorithms for data processing, performance profiling, and collaborating with various teams to influence future AI architecture designs.

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 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 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 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.

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 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 intern will develop a web-based configuration tool for high-performance computing appliances, ensuring user-friendly front-end design and complex backend logic. Responsibilities include collaborating with teams to gather technical requirements and generating precise outputs like Bill of Materials and configurations while adapting to customer needs.

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 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.

Assist in automating cluster configurations, contribute to upgrade workflows, support resource allocation systems, and help build monitoring tools for AI supercomputers at Cerebras Systems.

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 a Kernel Engineer, you will design and implement machine learning and linear algebra kernels, develop optimized assembly routines, measure software performance, and verify kernel libraries. You'll collaborate with system architects to optimize hardware and address challenges in machine learning applications.

The Data Processing Engineer will design and implement frameworks for visualizing and analyzing complex datasets, generate insights to improve system performance, and collaborate with cross-functional teams to meet their data needs. Responsibilities include ensuring data quality and integrity while querying data using SQL and processing it with Python.

The Detection and Response Lead at Cerebras Systems is responsible for architecting and operating cybersecurity detection and response capabilities. This role includes building a security analytics ecosystem, leading a blue team, implementing automation for threat detection, and improving response capabilities through integration and testing.