Design Verification Engineer

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.

Key Responsibilities

• Work with architects, designers, post silicon and software engineers to ensure a high-quality design that works for silicon.

• Develop and implement verification strategies, detailed tests and coverage plans based on micro-architecture.

• Create verification methodologies and reusable environments, including components such as stimulus, checkers, assertions, and coverage.

• Implement tests, manage regressions, gather coverage, and debug test failures.

• Collaborate with cross-functional teams including architecture, RTL design, physical design, firmware, and validation.

• Analyze and debug complex issues across simulation, emulation, and silicon bring-up phases.

• Continuously enhances verification infrastructure and flows to improve efficiency and quality.

• Contribute to the evolution of the overall verification methodology and best practices across the organization.

Skills and Qualifications

• Great debugging and problem-solving skills.

• Deep knowledge of SystemVerilog testbench, DPI and UVM.

• Excellent programming skills and knowledge of software engineering practices including object-oriented design.

• Experience developing scalable and portable testbenches and components.

• Experience with verification methodologies and tools such as simulators, waveform viewers, build and run automation, coverage collection, and gate level simulations.

• Proficient in scripting languages such as Python or Perl.