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About the Role

The AI Application Engineer will support the enablement, optimization, and deployment of AI models on automotive-grade SoCs. This role involves collaborating with internal compiler/runtime teams and external customers to transition AI models from training to optimized inference on embedded NPU/DSP platforms, focusing on performance, accuracy, and system integration.

Responsibilities

• Enable and deploy AI models such as BEV, object detection, segmentation, and classification on Gen4/5 SoC platforms.
• Analyze model performance including latency, throughput, and multi-core scaling to identify bottlenecks.
• Support model optimization workflows including Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), operator fusion, and graph optimization.
• Analyze and mitigate accuracy degradation caused by quantization or operator limitations.
• Integrate AI models into embedded Linux or QNX runtime environments.
• Debug issues related to NPU/DSP offloading, memory allocation, and data transfer overhead.
• Validate AI workloads on target boards and simulators.
• Work with AI compiler and runtime toolchains including ONNX-based workflows.
• Develop and maintain internal tools and scripts to improve validation and customer workflows.
• Act as a technical interface between customers, internal 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.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Embedded Systems, or equivalent 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 performance metrics such as latency, throughput, and hardware utilization.
• Good communication skills in a multi-cultural, cross-functional environment.

Skills

• Python
• C/C++
• PyTorch
• ONNX
• Embedded Systems
• Deep Learning
• Performance Analysis
• Quantization (PTQ/QAT)
• Linux/QNX
• Computer Vision

Location

• Hybrid

Work Type

• Full-time
• Regular (PERM)

Experience Level

• 1–3 years of experience in embedded systems or AI-related development preferred

Education Level

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or Embedded Systems

Benefits

• Remote work opportunities
• Inclusive workplace environment
• Employee resource groups
• Global support system

About the Company

• Renesas provides embedded semiconductor solutions for automotive, industrial, infrastructure, and IoT sectors.
• The company offers a portfolio including high-performance computing, embedded processing, analog, connectivity, and power solutions.
• Renesas operates in over 30 countries with over 22,000 employees.