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About the Role

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.

Responsibilities

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.
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).
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, and quantized (QDQ) ONNX models.
Develop or maintain internal tools and scripts to improve model validation, benchmarking, and customer workflows.
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.

Requirements

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.
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) or 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.
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.

Skills

AI models
Automotive-grade SoCs
NPU/DSP platforms
Performance analysis
Latency
Throughput
Multi-core scaling
Memory bandwidth
Scheduling
Operator mapping
Post-Training Quantization (PTQ)
Quantization-Aware Training (QAT)
Operator fusion
Graph optimization
Execution partitioning
Accuracy degradation analysis
Embedded runtime environments
Linux
QNX
CNNIP/DSP/NPU offloading
Memory allocation
IPMMU
Data transfer overhead
Multi-core synchronization
Target boards
Simulators (SIL / HIL)
AI compiler
Runtime toolchains
ONNX-based workflows
Hybrid compiler
MWMX
ONNX model handling
Graph inspection
Model segmentation
Execution control
Quantized (QDQ) ONNX models
Internal tools and scripts
Model validation
Benchmarking
Customer workflows
Technical interface
Customer evaluations
PoCs
Demos
Automotive AI platforms
Technical guidance
Documentation
Best practices
AI model deployment
Weekly technical reports
Issue tracking
Release validation
Deep learning fundamentals
Inference pipelines
PyTorch
ONNX
ONNX Runtime
Python
C/C++
Embedded systems
Debugging tools
Hardware utilization
Communication skills
Computer vision models
Detection
Segmentation
BEV
Automotive use cases
Robotics use cases
AI inference optimization
Embedded hardware
Quantization techniques
INT8
Calibration methods
Automotive SoCs
Safety-related software environments
Memory hierarchy
DMA
SoC architectures
Technical support
Application engineering
Automotive AI standards
ADAS perception pipelines
Intermediate representations

Work Type

Regular (PERM)
No Remote Work Available

Experience Level

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

Education Level

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Embedded Systems, or have experience in embedded systems.

Benefits

Competitive compensation
Comprehensive benefits package

About the Company

Renesas is a global company providing embedded semiconductor solutions, with the purpose of 'Making Our Lives Easier'.
With over 21,000 engineers and problem-solving professionals in more than 30 countries, we are involved in developing cutting-edge technologies in the automotive, industrial, infrastructure, and IoT fields, contributing to the realization of a safer, healthier, more environmentally friendly, and smarter future.
Renesas' core corporate culture is 'TAGIE' (Transparent, Agile, Global, Innovative, Entrepreneurial).
TAGIE represents the shared values that support our way of working, our growth, and our efforts to achieve our Purpose.
This collaborative spirit and challenging mindset enable us to drive industry transformation through semiconductor technology and contribute to people's lives worldwide.