Application Engineer

その他

フィールドアプリケーションエンジニア（FAE）

組み込みソフトウェアエンジニア

Job Description



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.


Qualifications



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.


Additional Information



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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