Image Algorithm Engineer

XG TECH PTE.LTD.; Santo Niño 1st; On-site; PHP 900,000 - 1,200,000; Full time

Job summary

XG Tech is seeking an Image Algorithm Engineer to develop and optimize computer vision algorithms for automotive applications, spanning cabin, parking, and vehicle security scenarios. You will optimize models for embedded automotive platforms and explore VLM/LLM and Agent applications for real-time, on-device AI.

Ideal candidates have 3+ years in CV algorithm development, with strong PyTorch/TensorFlow experience and exposure to ONNX, TensorRT, or TFLite toolchains.

Qualifications

• 3+ years of experience in computer vision algorithm development, with automotive cockpit exposure preferred.
• Strong knowledge of object detection, keypoint detection, segmentation, and tracking.
• Hands-on experience with training, optimization, and deployment on embedded platforms.

Responsibilities

• Develop and optimize computer vision algorithms for automotive applications, including DMS, AVM, Sentry, and privacy/de-identification features.

Skills

Object detection

Keypoint detection

Segmentation

Tracking

PyTorch

TensorFlow

Model training

Embedded platforms

VLM/LLM

Multimodal models

Tools

ONNX

TensorRT

TFLite

NPU toolchains

Job description

About Company

Founded in 2022, XG Tech is driving the future of smart vehicles. Its mission is to empower the digital transformation of automobiles, moving from distributed computing to a centralized, cross-domain platform.

XG Tech focuses on the intelligent cockpit - the next frontier of differentiation - while seamlessly integrating advanced driving systems. By reimagining cars as mobile living spaces, XG Tech aligns with the evolving trend of vehicles becoming the "third living space."

Role Summary

As an Image Algorithm Engineer, you will develop and optimize computer vision algorithms for automotive applications, covering cabin, parking, and vehicle security scenarios such as DMS, AVM, and Sentry. You will optimize vision models for embedded automotive platforms, while exploring VLM/LLM and Agent applications to enable intelligent multimodal interaction and real-time on-device AI.

Key Responsibilities

• Develop and optimize computer vision algorithms for automotive applications, including DMS, AVM, Sentry, and privacy/de-identification features.
• Design and optimize lightweight vision algorithms for embedded automotive platforms, addressing low compute, low latency, and high-reliability requirements.
• Build and manage the end-to-end vision data pipeline, including data collection, annotation standards, data cleaning, augmentation, model training, deployment, and iteration.
• Explore cockpit-driving integration applications, combining in-cabin perception, external environment sensing, and vehicle decision-making or autonomous driving systems.
• Develop VLM/LLM-powered automotive applications, including visual intent understanding, multimodal cockpit assistants, and real-time environmental information extraction.
• Explore vision-based AI Agents for proactive decision-making and context-aware interaction based on passenger and environmental states.
• Deploy and optimize VLM/LLM models on automotive edge chips, including model compression, inference acceleration, and heterogeneous computing for low-latency, low-power inference.

How You Will Stand Out

• 3+ years of experience in computer vision algorithm development, with at least 1 year of automotive cockpit experience such as DMS/OMS/IMS.
• Strong knowledge of object detection, keypoint detection, segmentation, tracking, and deep learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience with model training, optimization, and deployment on embedded platforms.
• Experience with ONNX, TensorRT, TFLite, or NPU toolchains for model conversion and acceleration.
• Experience integrating cabin perception with vehicle decision-making or autonomous driving systems.
• Experience with VLM/LLM applications, multimodal models, fine-tuning, or on-device inference optimization.
• Experience developing AI Agents, including task planning, memory, tool calling, streaming, or multimodal perception and decision-making.