Application Engineer (Automotive SoC)
2 months ago
Job SummaryWe are looking for an AI Application Engineer to support the enablement, optimization, and deployment of AI models on automotive-grade SoCs.....
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.
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