Renesas is one of the top global semiconductor companies in the world. We strive to develop a safer, healthier, greener, and smarter world, and our goal is to make every endpoint intelligent by offering product solutions in the automotive, industrial,...
About the role
Define and drive long-term AI software architecture strategy across model translation, compiler, runtime and deployment layers. Lead architecture decisions for heterogeneous AI deployment across CPUs, MCUs, MPUs, NPUs, DSPs, GPUs and future accelerator technologies. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Up to 30% international travel may be required. Travel is expected to support global engineering collaboration, architecture reviews, customer workshops, technical roadmap discussions, partner engagement, conferences and executive technical reviews
- Master's degree or PhD in Computer Science, Computer Engineering, Electrical Engineering or a related technical field
- Equivalent depth of technical achievement may be considered where supported by substantial industry experience and recognized architecture leadership
- 15+ years of professional software engineering experience preferred for the target profile
- 10+ years minimum experience in complex software systems, AI infrastructure, compiler technologies, embedded software, deployment platforms or related domains
- 5+ years of demonstrated technical leadership, architecture ownership or cross-team technical influence
More about this role
Renesas is seeking a Principal AI Software Architecture Engineer to lead the technical direction of next-generation AI software platforms and deployment technologies across embedded and edge computing products. The successful candidate will operate across software architecture, AI model deployment, compiler ecosystems, runtime integration, heterogeneous computing and hardware/software co-design.
Enable efficient and scalable deployment of AI workloads across diverse hardware platforms while creating a sustainable software foundation for future generations of AI tools, model translation workflows, runtime systems, development environments and customer-facing deployment solutions.
Define and drive long-term AI software architecture strategy across model translation, compiler, runtime and deployment layers.
Lead architecture decisions for heterogeneous AI deployment across CPUs, MCUs, MPUs, NPUs, DSPs, GPUs and future accelerator technologies.
Guide evolution of compiler and runtime technologies, including integration with LLVM/MLIR or equivalent compiler ecosystems where appropriate.
Collaborate with hardware, software, product, application engineering and customer-facing teams to...
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