Image Algorithm Engineer

Gateway Search; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

Gateway Search Pte Ltd in Singapore seeks an experienced computer vision engineer to design and optimize automotive vision systems, including driver monitoring and around-view features. You will own end-to-end data pipelines, model training, and on-device deployment for edge-friendly performance.

The role focuses on cockpit and driving integration, multimodal AI, and fast inference on embedded hardware, with opportunities to work on privacy-preserving features and real-time perception tasks.

Qualifications

• At least 3 years of experience in computer vision algorithm development.
• Automotive cockpit experience with DMS/OMS/IMS preferred (≥1 year).
• Strong understanding of object detection, keypoint detection, image segmentation, and tracking.
• Hands-on with PyTorch or TensorFlow.
• Experience in model training, optimization, and deployment on embedded/edge platforms.
• Familiarity with ONNX, TensorRT, TFLite or NPU toolchains for deployment.
• Experience integrating in-cabin perception with vehicle-level decisions or autonomous systems.
• Experience with multimodal AI (VLM/LLM) and on-device inference optimization.

Responsibilities

• Develop and optimize computer vision algorithms for automotive applications.
• Design lightweight vision algorithms for embedded platforms under resource constraints.
• Build and manage end-to-end vision data pipelines (data collection, annotation, cleaning, augmentation, training, deployment).
• Explore cockpit–driving integration of perception and decision-making.
• Develop VLM/LLM-powered automotive applications and multimodal perception.
• Develop vision-based AI agents for proactive decision-making.
• Deploy and optimize models on automotive edge computing platforms.

Skills

Computer vision

DMS experience

Automotive cockpit

PyTorch

TensorFlow

ONNX

TensorRT

TFLite

Edge AI

Multimodal AI

Tools

PyTorch

TensorFlow

ONNX

TensorRT

TFLite

Job description

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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

• 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.
• 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.
• Model Development & Deployment: Practical experience in model training, optimization, and deployment on embedded or edge computing platforms.
• Model Optimization Toolchains: Hands-on experience with ONNX, TensorRT, TFLite, or NPU-specific toolchains for model conversion, inference acceleration, and deployment.
• Automotive System Integration: Experience integrating in-cabin perception systems with vehicle-level decision-making or autonomous driving systems.
• Multimodal AI Experience: Experience developing VLM/LLM-based applications, working with multimodal models, model fine-tuning, or on-device inference optimization.
• 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.