Principal Computer Vision Engineer

Title: Principal Computer Vision Engineer

Location: Singapore

The Opportunity

RGE Digital is looking for a Principal Computer Vision Engineer to drive the development of automated agriculture and industrial AI solutions. In this high-impact role, you will lead the technical direction for deep learning initiatives, utilizing drone and satellite data to optimize operations across RGE's global portfolio, including forestry, manufacturing, and renewable energy.

About RGE Digital

RGE Digital enables the digital transformation of RGE's business units through sensors, analytics, cloud computing, and Artificial Intelligence. With operations spanning four continents with USD 40 billion in assets and 80,000 employees, we take a product-focused approach—prototyping, testing, and scaling advanced analytics capabilities to solve industry-defining challenges.

More details of RGE at: www.rgei.com; highlights of 2025: RGE 2025 Highlights

Key Responsibilities

• Architecture & Strategy: Lead the design and development of reliable AI vision models, ensuring scalability and high accuracy.
• Pipeline Development: Create end-to-end data collection, annotation, and MLOps pipelines for both edge and cloud environments.
• Implementation: Translate complex business requirements into efficient technical specifications and development roadmaps.
• Mentorship: Guide and mentor data scientists and engineers, fostering a culture of technical excellence.
• Innovation: Monitor the latest developments in AI and data science, integrating relevant technologies into RGE's ecosystem.

Qualifications

• 7+ years of professional experience developing Deep Learning/Computer Vision models deployed in production environments.
• Advanced degree (MS/PhD preferred) in Computer Vision, Machine Learning, or Computer Science.
• Expert proficiency in Python and frameworks such as PyTorch, TensorFlow, and OpenCV.
• Strong foundation in MLOps and data engineering best practices.
• Demonstrated ability to solve complex business problems with analytical precision.

Preferred Technical Skills

• Expertise in Foundation Models and Vision-Language Models.
• Familiarity with SOTA architectures such as DINOv3, SAM 3 (Segment Anything Model), DETR, and YOLO.
• Experience in Self-Supervised Learning and dealing with sparse or unlabelled data.
• Experience with Multi-spectral Satellite Imaging and geospatial data processing.

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