Application Engineer (Automotive SoC) at RenesasElectronics

Job Summary We are looking for an AI Application Engineer to support the enablement, optimization, and deployment of AI models on automotive-grade SoCs. In this role, you will work closely with internal compiler/runtime teams and external customers to bring AI models from training to optimized inference on embedded NPU/DSP platforms, with a strong focus on performance, accuracy, and system integration.   Key Responsibilities AI Model Enablement & Optimization • Enable and deploy AI models (e.g., BEV, object detection, segmentation, classification) on Gen4/5 SoC platforms with CNNIP/DSP/NPU HWA. • Perform model performance analysis (latency, throughput, multi-core scaling) and identify bottlenecks related to memory bandwidth, scheduling, or operator mapping. • Support model optimization workflows, including: • Post-Training Quantization (PTQ) • Quantization-Aware Training (QAT) collaboration • Operator fusion, graph optimization, and execution partitioning • Analyze accuracy degradation caused by quantization or operator limitations and propose mitigation strategies.Embedded AI Inference & System Integration • Integrate AI models into embedded runtime environments (Linux / QNX). • Debug issues related to: • CNNIP/DSP/NPU offloading • Memory allocation / IPMMU • Data transfer overhead and multi-core synchronization • Validate AI workloads on target boards and simulators (SIL / HIL).Toolchain & Model Workflow Support • Work with AI compiler and runtime toolchains (e.g., ONNX-based workflows, hybrid compiler, MWMX). • Support ONNX model handling, including: • Graph inspection and modification • Model segmentation and execution control • Quantized (QDQ) ONNX models • Develop or maintain internal tools and scripts to improve model validation, benchmarking, and customer workflows.Customer & Cross-Team Collaboration • Act as a technical interface between customers, internal development teams, and field application engineers. • Support customer evaluations, PoCs, and demos on automotive AI platforms. • Provide technical guidance, documentation, and best practices for AI model deployment. • Contribute to weekly technical reports, issue tracking, and release validation activities.