Application Engineer

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.

Responsibilities

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.; 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).; 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.; 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.

Requirements

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.

Nice to have

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, DMA, and multi-core scheduling in SoC architectures.; Experience supporting customers or acting in a technical support / application engineering role.; Knowledge of automotive AI standards or ADAS perception pipelines.; Experience contributing to internal tools, scripts, or documentation.; Ability to read and debug ONNX graphs or intermediate representations.