Job Description

Assistant Principal Integration AI Engineer

The Integration Engineer is responsible for end-to-end systems integration, field testing, and deployment of robotic capabilities developed at the Group Technology Office, AI.DA Strategic Technology Centre (STC), NEAR Lab. Working closely with the Programme Lead, you will translate research outputs into field-ready systems, manage integration timelines, and ensure programme deliverables are met on schedule and to specification.

You will integrate and deploy systems across drone and legged-robot platforms, supporting the operational evaluation of autonomous capabilities in complex environments. This is a hands-on role at the intersection of R&D and delivery. We are looking for a person who makes research work in the field.

The role is on a 2-year contract and is convertible to a permanent position based on good performance.

Key Responsibilities:

• Lead systems integration activities across robotic platforms (drones, quadrupeds, perception systems, compute).
• Plan and execute field trials and acceptance testing, coordinating logistics, safety, and documentation.
• Serve as the primary technical interface between NEAR Lab's R&D output and programme delivery requirements.
• Maintain integration logs, test records, and technical documentation to programme and customer standards.
• Identify and escalate integration risks early; propose and implement mitigations.
• Work with Capabilities tech leads to ensure research outputs are integration-ready for deployment.
• Support demonstrations and deployment at customer or field sites, including configuration, calibration, and on-site troubleshooting.

Required Qualifications:

• Bachelor's or Master's degree in Robotics, Electrical Engineering, Mechanical Engineering, Computer Engineering, or a related field.
• 1–3 years of experience in systems integration, robotics deployment, or a related engineering role. Fresh graduates with strong project experience are welcome to apply.
• Proficiency in Python for scripting, test automation, and data pipeline work. Familiarity with C++ in a ROS context is advantageous.
• Hands-on experience with ROS / ROS 2, including launch file management, TF transforms, and standard debugging tools (RViz, rosbag, rqt).
• Working knowledge of robot hardware bring-up: sensor calibration, coordinate frame configuration, and basic electronics (wiring, power systems, connector-level debugging).
• Familiarity with network configuration and communications relevant to multi-robot systems: IP networking, ROS 2 DDS tuning, and Wi-Fi or mesh network setup.
• Operational familiarity with SLAM and localisation systems: able to configure, monitor, and recover systems in the field.
• Strong documentation discipline and structured approach to testing and validation.
• Comfortable operating in field environments; willing to conduct outdoor trials.

Preferred Qualifications:

• Experience with edge compute platforms, particularly NVIDIA Jetson (Orin or Xavier): including embedded Linux, deployment toolchains (TensorRT, Docker), and onboard system management.
• Familiarity with AI inference pipelines: understanding model I/O, running inference on edge hardware, and debugging latency or failure at the deployment layer. Training experience is not required.
• Experience with embedded communications protocols (CAN bus, serial, I2C/SPI) relevant to actuator and sensor integration.
• Experience in a defence, public safety, or government programme context.