Early Engineering Partner (AI / Edge AI) – Remote

Others (General Work, Labour etc.) • Full Time • Hong Kong, Hong Kong, HK • 3w ago

GEOOE is an IP-first technology origin company, and brings together biomimetic thinking, distributed perspectives, mobility, and intelligence to shape a new approach to Instrumentation & Monitoring across Environmental, Geotechnical, Construction, Agricultural, and Smart City Domains.

We are now seeking an experienced engineer interested in participating in early-stage AI and Edge AI system development. This role is designed for engineers who enjoy building intelligent systems from the ground up, including AI model development, edge deployment, and real-world system integration. The role begins as a flexible engineering collaboration, working closely with the founder on early-stage AI architecture development and prototype validation. As the collaboration develops, there may be opportunities to grow into a long-term technical leadership role within the organization.

This is a remote position open to applicants worldwide. While we welcome candidates globally, we particularly encourage engineers based in the Greater Bay Area (Hong Kong, Macau, Shenzhen, and Guangzhou) to connect with us.

Candidates who have an interest in civil engineering, geotechnical engineering, environmental monitoring, or geological applications are especially encouraged to apply.

Responsibilities

Participate in the development of early-stage AI and Edge AI systems for engineering monitoring applications

Design and implement lightweight AI models suitable for edge deployment (Edge AI)

Contribute to data processing workflows, including data structuring, filtering, and feature extraction

Support the integration of AI models with embedded systems, sensors, and data acquisition devices

Assist in building prototype AI-driven demonstrator systems for technical validation

Work on system-level integration involving AI models, edge devices, and cloud interfaces (when applicable)

Support testing, debugging, and performance optimization of AI systems in real-world scenarios

Contribute ideas to improve model robustness, efficiency, and deployability in constrained environments

Collaborate directly with the founder on AI architecture design and implementation decisions

Requirements

Master’s or PhD degree in a relevant discipline such as:

Artificial Intelligence

Computer Science

Data Science

Electrical Engineering

Robotics

or other related technical fields

Practical experience in AI/ML development, Edge AI, or related technical areas
Familiarity with model deployment on edge devices (e.g., microcontrollers, embedded Linux systems, or similar platforms)
Experience with data processing, model training, and inference optimization
Ability to work independently in a remote engineering collaboration environment

Preferred Background

Experience with Edge AI frameworks (e.g., TensorFlow Lite, PyTorch Mobile, ONNX, etc.)

Knowledge of embedded AI deployment and resource-constrained optimization

Familiarity with wireless communication technologies such as BLE (for device integration)

Experience in IoT or sensor-based systems

Interest in engineering applications related to civil engineering, geotechnical engineering, environmental monitoring, or geological systems

Work Arrangement

Position type: Part-time or flexible collaboration

Location: Remote (global applicants welcome)

We particularly welcome candidates based in the Greater Bay Area (Hong Kong / Macau / Shenzhen / Guangzhou)

Working style: project-based technical collaboration

Start date: Flexible

Additional Note

This role is designed for engineers who are interested in contributing to early-stage AI and Edge AI development and exploring potential long-term technical collaboration as the technology and organization evolve.

How to Apply

If you are interested in connecting and learning more, please send a short introduction and relevant experience to:

[via CTgoodjobs Apply Now]

This opportunity remains open on a rolling basis as GEOOE continues to build a distributed engineering network.

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