Overview
We are seeking a talented and motivated Software Architect (AI Sub-system) to join our R&D team to own, define, and drive exploration and design of our next generation runtime software involving multi heterogeneous devices in high performance computing (HPC) SoCs.
Our division’s mission is to use the latest AI and cloud technologies to develop the best AI inference for advanced driver safety engineers building self-driving vehicles and other high performance compute products. Renesas is the leading automotive electronics supplier globally, and this is a rare opportunity to develop the infrastructure required to deploy our AI software to the billions of devices we ship to customers every year. You will join our newly formed AI & Cloud Engineering organization of around 100 software engineers. Due to strong demand for our AI-related products we are planning to triple in size in the next three years, so there is lots of room for you to help us grow the team together.
Responsibilities
· Participate and lead in the software architecture design of the runtime software across multi-device and heterogeneity.
· Apply code optimization techniques, ensuring efficient and optimized performance of the runtime software components.
· Deliver architecture specifications to developers and articulate them effectively across stakeholders ranging from toolchain, system and hardware teams, and to technology leadership.
· Collaborate with silicon and platform bring-up to verify and debug the AI sub-system and its delivered performance.
Qualifications
· Bachelor's or Master's degree in Computer Engineering, Electrical Engineering, or related field; Ph.D. is a plus.
· Great coding skills in C/C++.
· Great knowledge of SW architectural patterns (layered/plugin/event-driven/ microkernel/etc).
· Good knowledge of SDLC.
· Good knowledge of RTOS and/or Linux OS.
· Good knowledge of multi-thread and multi-process software design and debugging.
· Experience with embedded system involving multi-core CPU/DSP.
· Experience with heterogeneous multi-device system involving GPU/NPU/FPGA.
Knowledge of debuggers (LLDB, GDB) and profilers on heterogeneous hardware
