Lead AI/ML Solution Architect

Role – Lead AI/ML Solution Architect Stream - R&D Reporting- Director SoC Architecture Position Overview: This is a Lead/ Architect role in which you will be defining the architecture of the next generation of Machine Learning ASICs being built on the most modern process technologies and featuring industry leading performance and feature sets. Key Responsibilities :

• Work on modelling, performance analysis and architecture definition of Machine Learning ASICs.
• Map Autonomous driving workloads to heterogeneous ASICs that contain multiple different programmable processors and hardware accelerators. Perform detailed calculations to specify computation throughput, memory bandwidth and latency, evaluate performance v/s area v/s power tradeoffs.
• Identify appropriate workloads and micro-benchmarks to be used for performance analysis and drive this analysis on simulation and emulation platforms to define and validate the architecture.
• Evangelize your innovative architectural solutions with your peers and leadership, while mentoring members of the architecture team.
• Collaborate with cross functional teams working on RTL design, Design Verification, Firmware/Software development, Pre-Post silicon validation and Program Management to deliver first pass functional silicon on an aggressive schedule.
• Collaborate with software and firmware teams to ensure that the ASIC meets end to end application performance goals while maintaining ease and efficiency of software development.
• Conduct detailed performance, architectural and design requirement reviews with cross-functional teams.
• Lead hiring efforts, coach team members, and foster career growth through mentoring and training.
• Foster a culture of collaboration, oversee employee engagement initiatives, and conduct performance reviews.

• 12+ years of prior experience in defining and delivering multiple high performance ASICs into production, with focus on architecture definition and performance analysis.Domain knowledge in one or more of power/performance tradeoffs, digital signal processing, ML networks, ML frameworks such as Pytorch, Tensorflow etc.
• Experience and knowledge of Computer Architecture concepts such as microprocessor architecture, memory systems, on-chip interconnection networks, hardware/software partitioning etc.
• Programming in C or C++ with knowledge of mapping hardware algorithms to efficient C/C++ code.