Image Algorithm Engineer

Key Responsibilities

1. Computer Vision Algorithm Development: Develop and optimize computer vision algorithms for automotive applications, including Driver Monitoring Systems (DMS), Around View Monitoring (AVM), Sentry Mode, and privacy protection/de-identification features.
2. Embedded Algorithm Optimization: Design and optimize lightweight vision algorithms for automotive embedded platforms, addressing constraints in computing resources, inference latency, power consumption, and system reliability.
3. End-to-End Data Pipeline Management: Build and manage the complete vision data pipeline, covering data collection, annotation standards, data cleaning, augmentation, model training, deployment, and continuous performance improvement.
4. Cockpit–Driving Integration: Explore and develop integrated cockpit and driving applications by combining in-cabin perception, external environment sensing, and vehicle-level decision-making capabilities.
5. VLM/LLM Application Development: Develop automotive applications powered by Vision-Language Models (VLMs) and Large Language Models (LLMs), including visual intent understanding, multimodal cockpit assistants, and real-time environmental perception and information extraction.
6. Vision-Based AI Agent Development: Explore and develop vision-enabled AI Agents that support proactive decision-making and context-aware interactions based on passenger conditions and surrounding environments.
7. On-Device AI Deployment: Deploy and optimize VLM/LLM models on automotive edge computing platforms, leveraging model compression, inference acceleration, and heterogeneous computing architectures to achieve low-latency and power-efficient inference.

Requirements

1. Relevant Experience: At least 3 years of experience in computer vision algorithm development, including a minimum of 1 year of experience in automotive cockpit applications such as DMS, OMS, or IMS.
2. Computer Vision Expertise: Strong understanding of object detection, keypoint detection, image segmentation, and object tracking, with hands-on experience using deep learning frameworks such as PyTorch or TensorFlow.
3. Model Development & Deployment: Practical experience in model training, optimization, and deployment on embedded or edge computing platforms.
4. Model Optimization Toolchains: Hands-on experience with ONNX, TensorRT, TFLite, or NPU-specific toolchains for model conversion, inference acceleration, and deployment.
5. Automotive System Integration: Experience integrating in-cabin perception systems with vehicle-level decision-making or autonomous driving systems.
6. Multimodal AI Experience: Experience developing VLM/LLM-based applications, working with multimodal models, model fine-tuning, or on-device inference optimization.
7. AI Agent Development: Experience building AI Agent systems, with knowledge of task planning, memory management, tool calling, streaming interactions, and multimodal perception and decision-making.