Staff AI Application Engineer (Automotive SoC)

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 . What We Offer • Opportunity to work on state-of-the-art automotive AI platforms . • Hands-on involvement across the entire AI lifecycle , from model to silicon. • Close collaboration with compiler, runtime, and hardware architecture teams. • Exposure to real customer deployments and production constraints. Required Qualifications • Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Embedded Systems, or have experience in embedded systems . • Solid understanding of deep learning fundamentals and inference pipelines. • Hands-on experience with AI frameworks such as PyTorch, ONNX, or ONNX Runtime. • Strong programming skills in Python ; working knowledge of C/C++ is a plus. • Familiarity with embedded systems and debugging tools. • Ability to analyze performance using metrics such as latency, throughput, and hardware utilization. • Good communication skills in a multi-cultural, cross-functional environment. Preferred / Optional Qualifications • 1–3 years of experience in embedded systems or AI-related development . • Experience with AI model training, fine-tuning, or evaluation , especially for: • Computer vision models (Detection / Segmentation / BEV) • Automotive or robotics use cases • Practical experience with AI inference optimization on embedded hardware (NPU, DSP, GPU, or CPU). • Familiarity with quantization techniques (INT8, calibration methods, QDQ models). • Experience with automotive SoCs or safety-related software environments (QNX is a plus). • Understanding of memory hierarchy, D