Renesas Electronics
Technology
ApplicationEngineer(AutomotiveSoC)
Neural analysis suggests this role is
optimal for mid candidates.
“Application Engineer (Automotive SoC) at Renesas Electronics. Skills: AI Model Enablement, Embedded AI Inference, Toolchain Support. Enable and deploy AI models. Perform model performance analysis”
Industry & Context.
Analyze performance; Identify bottlenecks; Propose mitigation strategies; Debug issues
What They're Looking For.
Must Have
Bachelor’s or Master’s degree, Solid understanding of deep learning fundamentals, Hands-on experience with AI frameworks, Programming skills in C/C++, Familiarity with embedded systems, Ability to analyze performance
Nice to Have
1–3 years of experience, Experience with AI model training, Computer vision models, Automotive or robotics use cases, Practical experience with AI inference optimization, Familiarity with quantization techniques, Experience with automotive SoCs, QNX is a plus, Understanding of memory hierarchy, Experience supporting customers, Knowledge of automotive AI standards, Experience contributing to internal tools, Ability to read and debug ONNX graphs
What You'll Do.
Enable and deploy AI models
Perform model performance analysis
Support model optimization workflows
Analyze accuracy degradation
Propose mitigation strategies
Integrate AI models into runtime environments
Debug issues related to offloading
Debug memory allocation
Debug multi-core synchronization
Validate AI workloads
Work with AI compiler toolchains
Support ONNX model handling
Develop or maintain internal tools
Act as a technical interface
Support customer evaluations
Provide technical guidance
Contribute to technical reports
Contribute to issue tracking
Contribute to release validation
How You'll Work.
Team & Collaboration
Internal compiler/runtime teams; External customers; Cross-functional environment; Field application engineers
Communication Scope
Technical guidance; Documentation; Best practices
Full 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
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