ML Accelerator Architect and Performance Engineer, Silicon

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Science, Image Processing, or equivalent practical experience.
  
• 5 years of relevant work or academic research experience in computer or chip architecture, performance, and/or compiler.
• Experience with one or more general purpose programming languages including (but not limited to) C/C++ or Python and deep learning frameworks like TensorFlow/Jax/Pytorch.

Preferred qualifications:

• Master or PhD degree in Computer Science, Electrical Engineering or related field, or equivalent practical experience.
• Experience with hardware/software co-design for machine learning.
• Experience with simulator development and micro-architecture.
• Experience with distributed/parallel programming.
• Experience with domain-specific accelerators.

In this role, you will focus on exploring future hardware and software architecture to efficiently enable on-device machine learning applications. Using your expertise in neural network models, hardware architecture, compilers, and software stacks, you will drive hardware architecture exploration, and also contribute to compiler, runtime, and API strategies, as well as other innovations.

Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology.

• Drive hardware architecture exploration while collaborating with research teams, system architecture teams, and compiler engineers to optimize future workloads from both software and hardware perspectives
• Initiate new feature modeling in the architecture simulator and optimize the performance by collaborating with researchers and application developers to enable the latest machine learning work.
• Enhance user experiences by working collaboratively with full stack software engineers to partition machine learning workload to selected compute engines.
• Engage with and learn from talented researchers and engineers across Google.