About the job
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.
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, DMA, and multi-core scheduling in SoC architectures.
Nice to Have
- 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.
ルネサスは、「To Make Our Lives Easier（人々の暮らしをより豊かで快適にする）」というPurposeのもと、組込み半導体ソリューションを提供するグローバル企業です。世界30か国以上で活躍する21,000人を超えるエンジニアや課題解決のプロフェッショナルとともに、自動車、産業、インフラ、IoT分野における世界最先端のテクノロジー開発に携わり、より安全で、健康的で、環境にやさしく、スマートな未来の実現に貢献しています。
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