Application Engineer

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.

Required Qualifications

• 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.

Preferred / Optional Qualifications

• 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)
  • 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.

Nice to Have

• 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.

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