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。
ルネサスでは、「TAGIE（Transparent、Agile、Global、Innovative、Entrepreneurial）」を企業文化の中核としています。TAGIEは、私たちの働き方や成長のあり方、そしてPurposeの実現に向けた取り組みを支える共通の価値観です。この協調的な精神と挑戦するマインドセットが、半導体技術を通じた産業の変革と、世界中の人々の暮らしへの貢献を可能にしています。
私たちは、競争力のある報酬制度に加え、充実した福利厚生をご用意しています。福利厚生の詳細については、選考プロセスの中でご案内いたします。
私たちとともに未来を創造する挑戦に、ぜひ参加しませんか。皆さまからのご応募をお待ちしております。
Skills Required
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or Embedded Systems, or equivalent embedded systems experience
- 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
- Familiarity with embedded systems and debugging tools
- Ability to analyze latency, throughput, and hardware utilization performance metrics
- Good communication skills in multicultural, cross-functional environments
- One to three years of experience in embedded systems or AI-related development
- Experience with AI model training, fine-tuning, or evaluation
- Experience with computer vision models including detection, segmentation, or BEV
- Experience with automotive or robotics use cases
- Practical experience optimizing AI inference on embedded NPU, DSP, GPU, or CPU hardware
- Familiarity with quantization techniques including INT8, calibration methods, and QDQ models
- Experience with automotive SoCs or safety-related software environments
- Experience with QNX
- Understanding of memory hierarchy, DMA, and multicore scheduling in SoC architectures
- Experience supporting customers or working in technical support or application engineering
- Knowledge of automotive AI standards or ADAS perception pipelines
- Experience developing internal tools, scripts, or documentation
- Ability to read and debug ONNX graphs or intermediate representations
Renesas Electronics Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Renesas Electronics and has not been reviewed or approved by Renesas Electronics.
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Fair & Transparent Compensation — Feedback suggests pay is generally fair-to-good across many roles, with base pay aligned to prevailing market levels in multiple U.S. locations. Engineering and leadership tracks can be competitive on total compensation, reinforcing a sense that pay can meet market expectations in key job families.
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Healthcare Strength — Feedback suggests core medical, dental, and vision coverage is solid and well-regarded by employees. Day-one eligibility in U.S. roles is described, supporting confidence in access and continuity of care.
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Leave & Time Off Breadth — Feedback suggests PTO, paid holidays, sick time, and parental leave provide a comprehensive time-off framework. Company initiatives like Renesas Day and meeting-light Focus Fridays further support time away and balance.
Renesas Electronics Insights
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