Total 1 Transportation / Logistics job vacancies in Musashino, Tokyo

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 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.