Principal AI Accelerator Architect

Principal Ai Accelerator Architect

Our architecture removes many of the constraints imposed by conventional collections of packaged processors and creates a fundamentally different design space spanning compute, memory, communication, programmability and system scaling.

We are looking for an experienced architect to conceive and drive new capabilities for our next-generation AI accelerators. This is an end-to-end architecture role: you will identify opportunities from important AI workloads, quantify their performance and efficiency value, develop the microarchitecture and establish implementation feasibility across hardware and software.

You will own architectural features from initial insight through a well-grounded proposal that is ready for implementation.

What You'll Do

• Analyze AI workloads, kernels and system bottlenecks to identify high-value architectural opportunities.
• Quantify feature value using targeted performance models, workload studies and representative application scenarios.
• Translate architectural ideas into concrete microarchitecture, including datapaths, control, memory behavior, data movement and interfaces.
• Evaluate interactions with the compiler, runtime, programming model and performance-critical kernels.
• Develop, or partner with implementation teams to develop, targeted prototypes, RTL experiments, synthesis studies or analytical models to validate feasibility and estimate power, performance and area.
• Explore architectural alternatives and clearly articulate their benefits, costs, risks and implementation trade-offs.
• Write architectural and microarchitectural specifications that enable RTL, verification, compiler, kernel and system teams to execute.
• Lead cross-functional technical reviews and drive decisions from initial proposal through implementation handoff.
• Partner with implementation teams during development to preserve the architectural intent and resolve issues that emerge.

What We're Looking For

• 10+ years of experience in computer architecture, microarchitecture or processor development, including work with CPUs, GPUs, AI accelerators or other high-performance processors.
• Deep understanding of computer architecture and microarchitecture.
• A track record of taking architectural features from workload need or initial concept through microarchitecture definition and implementation.
• Strong quantitative judgment, including the ability to evaluate performance benefits against power, area, complexity and programmability costs.
• Experience using targeted hardware performance models and architecture-exploration tools to evaluate specific features, using Python, C++ or similar environments.
• Ability to reason across workloads, kernels, memory systems, interconnects and compute pipelines.
• Working knowledge of RTL, synthesis, timing, power and physical-design considerations sufficient to establish implementation feasibility.
• Experience collaborating closely with RTL, verification, physical-design, compiler, kernel and system-software teams.
• Strong written and verbal communication skills, including the ability to make and defend consequential architectural decisions.
• MS or PhD in Electrical Engineering, Computer Engineering, Computer Science or equivalent practical experience.

Particularly Relevant Experience

• Architecture or microarchitecture development for machine-learning accelerators, GPUs, CPUs, vector processors or network processors.
• Transformer inference or training, including attention, matrix and vector operations, collectives, sparsity, quantization or mixture-of-experts workloads.
• Memory hierarchy, on-chip networks, DMA engines, distributed compute or large-scale accelerator systems.
• Kernel development, compiler interaction or performance optimization close to the hardware.

This is an architecture role with hands-on feasibility work. You may develop targeted RTL, synthesis or modeling experiments to answer specific architectural questions, but the position is not centered on production RTL implementation, block-level design ownership or ownership of the broader performance-modeling infrastructure.

The central responsibility is to ensure that proposed features are valuable, programmable, implementable and ready for execution.

The base salary range for this position is $200,000 to $300,000 annually. Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we've reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

1. Build a breakthrough AI platform beyond the constraints of the GPU.
2. Publish and open source their cutting-edge AI research.
3. Work on one of the fastest AI supercomputers in the world.
4. Enjoy job stability with startup vitality.
5. Our simple, non-corporate work culture that respects individual beliefs.

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.