SrEngineer

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