Processor Performance and Modeling Architect

As a Processor Performance and Modeling Architect at Rain AI, your responsibilities will include proposing and implementing custom extensions to processor cores that accelerate AI workloads. You'll also be involved in developing models that facilitate the implementation and testing of these features, alongside collaborating with hardware and software teams to ensure proper integration within Rain's technological framework.

To qualify for the Processor Performance and Modeling Architect position at Rain AI, candidates should possess a Master’s degree (MS) with at least five years of experience or a PhD in Electrical or Computer Engineering. Knowledge of processor architecture, AI/ML accelerators, and familiarity with programming in C++ and SystemC are also important for success in this role.

Absolutely! Rain AI offers a remote work opportunity for the Processor Performance and Modeling Architect role, allowing you to work from anywhere in the United States. This gives you the flexibility to contribute to our projects while maintaining a healthy work-life balance.

As a Processor Performance and Modeling Architect at Rain AI, you can look forward to comprehensive benefits, including 100% medical insurance premium coverage, dental and vision insurance, a 401k match, unlimited PTO, and special company-wide time off around holidays. Additionally, you’ll receive a budget for office equipment to enhance your remote work setup.

Yes, while not mandatory, prior startup experience is preferred for the Processor Performance and Modeling Architect position at Rain AI. This experience can provide valuable insights into the fast-paced and innovative nature of a startup environment, which aligns well with our company culture.

When answering this question, consider detailing specific projects you've worked on, highlighting your experience with both RISC-V architecture and custom processor extensions. Discuss the impact your work had on performance improvements in AI workloads.

In your response, outline your design approach starting from requirements analysis to simulation testing. Mention methodologies like co-design practices you’ve implemented to balance performance and power in your designs.

To answer, provide a brief overview of the types of AI/ML workloads you've encountered and how understanding these workloads influences your design choices. Discuss how this knowledge can enhance processing efficiency and optimize software-hardware integration.

Here, emphasize specific modeling tools you have used, such as SystemC or any custom simulation tools. Discuss how these tools have assisted you in validating architecture performance, and perhaps reference a project where these skills were crucial.

Effective communication is key, so mention strategies like regular meetings, shared documentation, and using collaborative tools. Share an example of a project where clear communication greatly contributed to successful integration.

Reflect on specific challenges such as unforeseen performance issues or compatibility concerns with existing systems. Discuss how you navigated these challenges and what solutions led to a successful outcome.

Here, explain your approach to creating clear and comprehensive documents. Mention the key components you typically include, and how this documentation serves both present and future engineering teams for understanding key architecture specifications.

When addressing this question, choose a specific project, detailing the objectives, strategies, and the quantifiable outcomes of your performance improvements. Highlight the methodologies and tools that were instrumental to your success.

Discuss your experience with parallel processing concepts, illustrating any practical applications where you’ve successfully implemented SIMD, multi-threading, or vector architectures to optimize processor performance for AI workloads.

In your response, express your enthusiasm for participating in cutting-edge AI technology development. Connect your goals with Rain AI’s mission of creating efficient hardware for AI, highlighting your passion for innovation and contribution to a transformative industry.