Principal AI Software Architecture Engineer (f/m/d)

Job description

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. Mission 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.

Key Responsibilities

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 translate platform needs into executable technical roadmaps.

Drive hardware/software co-design discussions to influence future AI acceleration and software platform capabilities. Mentor senior engineers and technical leads, helping shape architecture thinking across globally distributed teams. Represent the technical direction in architecture reviews, roadmap discussions, customer workshops, partner discussions and executive-level technical reviews. Travel Requirement 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.

Qualifications

Education

Requirements 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.

Experience

Requirements 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. Experience leading architecture decisions that affected multiple teams, product generations, platforms or customer-facing software ecosystems.

Experience working in globally distributed engineering environments. Language Requirements Fluent English, spoken and written, is required. Additional language capabilities, such as Japanese, German, French or other business-relevant languages, are beneficial but not mandatory. Required Technical Expertise Large-scale software architecture and platform evolution. AI model deployment workflows and AI software toolchains. Compiler ecosystems such as LLVM, MLIR, IREE, XLA, TVM, OpenXLA or comparable technologies.

Programming expertise in C++ and Python. AI frameworks and model formats such as PyTorch, TensorFlow, ONNX and TFLite. Runtime integration, performance engineering, profiling, memory optimization and latency/throughput analysis. Embedded and edge computing environments, including constraints around memory, power, performance and deployment workflow. Heterogeneous hardware architectures including CPU, MCU, MPU, NPU, DSP, GPU and accelerator-based systems.

Preferred Qualifications

Experience defining multi-year technical roadmaps. Experience with customer-facing technical leadership, strategic workshops or partner ecosystem discussions. Experience mentoring senior engineers, technical leads or future architects. Contributions to compiler, AI deployment, runtime, embedded software or open-source ecosystems. Patents, publications or recognized technical contributions are advantageous but not required if strong architecture ownership is demonstrated.

Candidate Profile The ideal candidate is a proven software architecture leader with deep understanding of AI deployment and strong compiler knowledge. The role is best suited to a systems thinker who can connect software platforms, model translation workflows, compiler ecosystems, runtime behavior, hardware constraints, customer needs and long-term product strategy. Success Metrics Accelerate AI deployment across embedded and edge hardware platforms.

Improve maintainability and adaptability of AI software platforms and deployment technologies. Establish clear architecture direction across toolchains, runtimes, model translation and heterogeneous execution. Improve collaboration between hardware, software, product and customer-facing organizations. Provide credible long-term technical leadership for future AI software platform evolution.