Application Engineer - Kodaira, Japan
Originally posted 2 September 2026 by the employer.
About the role
This role involves supporting the enablement, optimization, and deployment of AI models on automotive-grade SoCs.
What you'll do
- Enable and deploy AI models on Gen4/5 SoC platforms with CNNIP/DSP/NPU HWA.
- Perform model performance analysis 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.
- Integrate AI models into embedded runtime environments (Linux / QNX) and debug issues related to CNNIP/DSP/NPU offloading, memory allocation / IPMMU, data transfer overhead, and multi-core synchronization.
- Work with AI compiler and runtime toolchains (e.g., ONNX-based workflows, hybrid compiler, MWMX) and support ONNX model handling, including graph inspection, modification, segmentation, execution control, and Quantized (QDQ) ONNX models.
- Act as a technical interface between customers, internal development teams, and field application engineers, supporting customer evaluations, PoCs, and demos on automotive AI platforms.
What you'll need
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Embedded Systems, or 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++.
- Familiarity with embedded systems and debugging tools.
- Ability to analyze performance using metrics such as latency, throughput, and hardware utilization.
Nice to have
- 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).
- 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, Gen4/5 SoC platforms, CNNIP/DSP/NPU HWA, model optimization, embedded AI inference, automotive AI platforms
This role has been open 55 days — well below the 89-day median for AI/ML Hardware Engineering roles.
AI/ML Hardware Engineering · AI ML Hardware
|
Open roles in category
507
|
Median days open
89 d
|
Median salary
$236k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | Renesas Electronics | All employers we track in this specialty (507 roles · 76 employers) |
|---|---|---|
| Open roles in this specialty | 5 | 507 |
| Open roles in the wider Software, Firmware & Systems family | 118 | 6268 · 146 employers |
| Median days open | 39 d | 89 d (−50 d vs this employer) |
| Median salary (USD postings) | — | $236k |
Skills observed across this category: AI models, Gen4/5 SoC platforms, CNNIP/DSP/NPU HWA, model optimization, embedded AI inference, automotive AI platforms
Who's hiring in this category
- Qualcomm · 104 open roles · median 118 d
- NVIDIA · 88 open roles · median 60 d
- AMD · 32 open roles · median 110 d
- Micron Technology · 27 open roles · median 62 d
- Mobileye · 20 open roles · median 88 d
- Analog Devices · 18 open roles · median 45 d
How we counted: 507 open AI/ML Hardware Engineering (AI ML Hardware) roles from 76 employers tracked in the SemiconductorJobs index, counted 27 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.