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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
• Embedded 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
• AI frameworks
• Computer vision models
• Detection
• Segmentation
• BEV
• Automotive use cases
• Robotics use cases
• AI inference optimization
• Embedded hardware
• NPU
• DSP
• GPU
• CPU
• Quantization techniques
• INT8
• Calibration methods
• QDQ models
• Automotive SoCs
• Safety-related software environments
• Memory hierarchy
• DMA
• Multi-core scheduling
• SoC architectures
• Technical support
• Application engineering
• Automotive AI standards
• ADAS perception pipelines
• Internal tools
• Scripts
• ONNX graphs
• Intermediate representations

Work Type

• Hybrid

Experience Level

• 1-3 years

Education Level

• Bachelor’s degree
• Master’s degree

About the Company

• Renesas provides embedded semiconductor solutions under the purpose of 'To Make Our Lives Easier'. As a leader in high-quality, system-level embedded semiconductors, we offer scalable and comprehensive solutions centered around a broad product portfolio including high-performance computing, embedded processing, analog & connectivity, and power for automotive, industrial, and infrastructure & IoT applications.
• With over 22,000 diverse employees in more than 30 countries, Renesas challenges the limits, enhances user experiences through digitalization, and pioneers a new era of innovation. We are fully committed to developing sustainable and energy-efficient solutions for the future of people and communities worldwide, realizing 'To Make Our Lives Easier'.
• What you can achieve at Renesas:
• Start and advance your career: You can gain experience as a technical professional and in a wide range of business areas across our four product groups and various other divisions. You will have opportunities to deepen your expertise in hardware and software or take on new challenges.
• Do meaningful and impactful work: By being involved in the development of innovative products and solutions, you can meet the needs of customers worldwide while contributing to making people's lives more convenient, safe, and secure.
• Maximize your potential in an environment focused on well-being: At Renesas, we aim to create an inclusive workplace by supporting flexible work arrangements, such as a remote work system, and actively supporting employee resource groups. Our employee-first culture and global support system provide an environment where you can thrive from the moment you join.
• Are you ready to seize success and build your career on your own terms?
• Let's shape the future together at Renesas.
• We have adopted a hybrid work model, allowing employees to work remotely two days a week. Simultaneously, the remaining days are spent in the office as a team to strengthen collaboration. Designated office days are Tuesday through Thursday, dedicated to innovation, collaboration, and continuous learning.