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.

Qualifications :

Required Qualifications

• Bachelors or Masters 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

• 13 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.

Required Qualifications

• Bachelors or Masters 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

• 13 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 人々の暮らしを楽ラクにするというPurposeの下組み込み半導体ソリューションを提供します高品質とシステムレベルノウハウを兼ね備えた組み込み半導体のリーダーとして自動車産業インフラIoT分野向けにハイパフォーマンスコンピューティング組み込みプロセッシングアナログコネクティビティそしてパワーを含めた幅広い製品ポートフォリオを軸としたスケーラブルで包括的なソリューションを提供しています
 

ルネサスは30か国以上で22000 人を超える多様性あふれる従業員と共に限界に挑戦しながらデジタライゼーションを通じてユーザエクスペリエンスを充実化させ新たなイノベーションの時代を切り開いていきますそして世界中の人々やコミュニティの未来のために持続可能で省エネ効果の高いソリューションの開発に全力で取り組みTo Make Our Lives Easierを実現します    
 

ルネサスで実現できること
 

• キャリアをスタートそしてキャリアアップ4つのプロダクトグループをはじめさまざまな部門において技術職としてまた幅広いビジネスの経験を積むことができますハードウェアソフトウェアの専門知識を深めたり新しいことにチャレンジしたりする機会があります 
   
• やりがいとインパクトのある仕事をする 革新的な製品とソリューションの開発に関わることにより世界中のお客様のニーズに応えると同時に人々の生活をより便利で安全かつ安心なものにすることに貢献できます
   
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自分の力で成功を掴みキャリアを築く準備はできていますか 

ルネサスで一緒に未来を形づくっていきましょう

当社ではハイブリッド勤務モデルを採用しており従業員は週に2日間リモートワークを行うことができます同時に残りの日はチームとしてオフィスに集まり協働を強化しています出社指定日は火曜日から木曜日でイノベーションコラボレーションそして継続的な学習に取り組む日としています

Remote Work :

No

Employment Type :

Full-time