Intern, AI/ML Compiler Research Engineer

Please Note:

To provide the best candidate experience amidst our high application volumes, each candidate is limited to 10 applications across all open jobs within a 6-month period. 

Advancing the World’s Technology Together

Our technology solutions power the tools you use every day--including smartphones, electric vehicles, hyperscale data centers, IoT devices, and so much more. Here, you’ll have an opportunity to be part of a global leader whose innovative designs are pushing the boundaries of what’s possible and powering the future. 

We believe innovation and growth are driven by an inclusive culture and a diverse workforce. We’re dedicated to empowering people to be their true selves. Together, we’re building a better tomorrow for our employees, customers, partners, and communities.

What You’ll Learn

• Project Overview: Contribute to the design and implementation of new DSLs, compiler passes and optimizations to improve performance, efficiency, and resource utilization for executing LLM workloads on next-generation accelerators.
• Skills You’ll Learn:
  • Co-design of Memory-centric AI accelerator architectures and platforms
  • Optimizing the execution of state-of-the-art Generative AI models on specialized hardware
  • Hardware-software co-design and co-optimization

What You’ll Do

The AGI (Artificial General Intelligence) Computing Lab is dedicated to solving the complex system-level challenges posed by the growing demands of future AI/ML workloads. Our team is committed to designing and developing scalable platforms that can effectively handle the computational and memory requirements of these workloads while minimizing energy consumption and maximizing performance. To achieve this goal, we collaborate closely with both hardware and software engineers to identify and address the unique challenges posed by AI/ML workloads and to explore new computing abstractions that can provide a better balance between the hardware and software components of our systems. Additionally, we continuously conduct research and development in emerging technologies and trends across memory, computing, interconnect, and AI/ML, ensuring that our platforms are always equipped to handle the most demanding workloads of the future. 

The ideal candidate for this role would share our passion for creating and increasing the value of new technologies and products, and thrive in a highly dynamic, fast-paced, results-driven environment. We are looking for someone to work with a group of highly talented, passionate, and versatile engineers who share our vision and contribute to the creation of next generation memory-centric AI solutions/products. Join us in our passion to shape the future of computing! 

Location: Daily onsite presence at our San Jose, CA headquarters in alignment with our Flexible Work policy

Reports to: Director, SW Dev. Infrastructure Engineering

• Research, develop and evaluate novel compiler-based solutions for efficiently executing emerging Generative AI models on next-generation memory-centric accelerator architectures.
• Profile computational kernels written using different DSLs and analyze/debug functional and performance bottlenecks on target hardware platforms.
• Work with cutting-edge technologies and talented professionals to prepare a novel presentation for our annual Intern Exhibition, draft novel patents and scientific publication to be submitted to top-tier conferences. 
  
• Complete other responsibilities as assigned.

What You Bring

• Pursuing a PhD in Computer Science, Computer Engineering or related field, with focus on AI/ML Compiler technologies and deep learning models.
• Must have at least 1 academic quarter/semester remaining.
• Experience with AI/ML compiler optimizations like tiling, vectorization, parallelization, and quantization.
• Demonstrated knowledge of computer architecture, with a focus on GPUs or AI hardware accelerators and state-of-the-art domain-specific parallel programming languages.
• Prior first author publications in top-tier AI/ML conferences.
• Prior contributions to open-source projects in the AI/ML compiler space such as LLVM, MLIR, Triton, PyTorch Inductor, etc., is a big plus.
• Must be highly motivated with excellent verbal and written communication skills, and ability to thrive in a collaborative, multi-disciplinary environme