AI SW Architecure Intern

The AI Software Architecture Intern at d-Matrix plays a key role in developing and optimizing memory management techniques. This position involves enhancing the integration of accelerators into the PyTorch ecosystem, specifically focusing on execution, representation, and memory management practices. Interns will interact closely with the software stack and PyTorch internals, gaining valuable insights about LLM inference and our unique computing architecture.

Candidates for the AI Software Architecture Intern position at d-Matrix should have a solid understanding of programming in C++ and Python. Familiarity with PyTorch is essential, as you'll be working with its internals. Additionally, an interest in exploring memory management techniques and multilevel memory architectures will be beneficial. A proactive, problem-solving attitude and a willingness to learn are also important attributes for success in this role.

At d-Matrix, the work environment is collaborative and inclusive, with a culture that values respect, humility, and open communication. The AI Software Architecture Intern position is hybrid, requiring onsite work 3-5 days a week at our Santa Clara, CA headquarters. This setup allows interns to engage directly with team members while also enjoying some flexibility in their work arrangements.

As an AI Software Architecture Intern at d-Matrix, you will work on projects that involve enhancing the integration of accelerators into the PyTorch ecosystem. You will explore new memory management techniques that leverage our unique multi-level memory architecture. This will provide you with hands-on experience in optimizing software performance and a deep understanding of cutting-edge AI technologies.

Applicants interested in the AI Software Architecture Intern role at d-Matrix can prepare by brushing up on their C++ and Python programming skills, as well as gaining a solid understanding of the PyTorch framework. It might also be helpful to learn more about memory management techniques and how they apply to hardware acceleration. Familiarizing oneself with large language models and their inference mechanisms will give a competitive edge going into the interview process.

When answering this question, highlight specific projects or coursework where you've utilized C++ and Python. Discuss any libraries or frameworks you’ve worked with, particularly those related to AI and machine learning, to demonstrate your hands-on experience.

For this question, discuss techniques such as model quantization, pruning, increasing batch size, or leveraging multi-GPU setups. Provide examples from your previous work or studies and explain your thought process in approaching optimizations.

In your answer, touch upon the concepts of memory allocation, garbage collection, and optimizing memory usage in applications. Discuss how understanding these concepts applies to developing efficient AI models and how they can impact performance.

Share any relevant coursework, projects, or self-study you’ve undertaken regarding large language models. Outline the key features of their architecture and the challenges faced during inference, and discuss your interest in this area of AI development.

Use the STAR method (Situation, Task, Action, Result) to detail a specific challenge. Highlight your problem-solving skills, technical knowledge, and how you proactively sought solutions to resolve the issue.

Express your enthusiasm for d-Matrix's focus on innovation in generative AI, emphasizing how it aligns with your career goals. Discuss the appeal of their collaborative culture, the opportunity to work with cutting-edge technology, and their commitment to diversity and inclusion.

Outline a strategy that might involve understanding the existing ecosystem, identifying areas for enhancement, and collaborating with cross-functional teams. Highlight your technical knowledge and any previous experience related to software integration or hardware acceleration.

Discuss practices like code reviews, unit testing, and following coding standards. Emphasize the importance of maintaining high quality in software development, especially in AI, where inefficiencies can significantly impact performance.

Explain your experience using Git for managing code, including branching, merging, and resolving conflicts. Share examples of how you’ve effectively collaborated with teams through version control in past projects.

Discuss your passion for AI, technology, and problem-solving. Mention any specific influences or experiences that drew you to this field, and express your desire to contribute positively to the innovations happening at d-Matrix.