Simulation Engineer

About the role

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.

Responsibilities

• Develop and maintain simulator infrastructure in C++ for next-generation WSE systems.
• Contribute to both functional simulation (architectural correctness, instruction behavior, and state and memory semantics) and pipeline-accurate simulation (execution behavior, bottleneck analysis, and performance visibility).
• Build features and tooling that improve kernel developer visibility into correctness and performance.
• Work with Design Verification and architecture teams to align simulator behavior with hardware specifications and validate design correctness.
• Build and maintain strong unit, integration, and regression test coverage for simulator quality.
• Improve simulator runtime performance, scalability, and usability for internal engineering workflows.
• Debug complex issues that span simulator models, kernels, compiler/runtime interactions, and hardware assumptions.
• Contribute to code reviews, documentation, and continuous improvement of engineering workflows.

Skills & Qualifications

• Bachelor's or Master's degree (or equivalent) in Computer Science, Computer Engineering, or a related field.
• Strong C++ programming skills and software engineering fundamentals.
• Solid understanding of computer architecture concepts, including instruction execution, memory systems, and microarchitecture basics.
• Strong debugging, analytical, and problem-solving skills.
• Able to collaborate effectively across software and hardware teams.

Preferred Skills & Qualifications

• Familiarity with architectural, functional, or performance modeling.
• Experience with test automation, regression systems, and CI pipelines.
• Exposure to Python or scripting for tooling and automation.
• Familiarity with parallel systems, accelerators, or ML/HPC workloads.