Electronics & Product Commercialisation Engineer

About RossAi

RossAi specializes in developing AI-driven sensor products that operate in real time on edge devices. Their camera systems, WasteLens and OpsLens, provide live video analytics without cloud dependence, serving industries like waste management, manufacturing, agritech, viticulture, and horticulture across New Zealand. The company values small teams and practical hardware deployments in the field.

Role Overview

This position involves spearheading the development and refinement of commercial hardware products based on the WasteLens prototype. As an Electronics & Product Commercialisation Engineer, you will be responsible for the research and development of sensor and camera electronic hardware, circuit boards, enclosures, and rigorous testing to ensure durability under harsh environmental conditions.

This hands-on engineering role aims to transition a prototype into a consistently manufacturable and deployable product.

Key Responsibilities

• Designing sensor integrated circuits and mixed-signal PCBs including schematic capture, layout, bring-up, and troubleshooting for camera and sensor boards.
• Evaluating and selecting edge hardware components for on-device AI inference, balancing constraints like quantisation and processing trade-offs.
• Developing rugged enclosures suitable for industrial environments, addressing thermal management, ingress protection, connector access, and manufacturability.
• Integrating sensors and prototyping edge devices.
• Conducting structured hardware testing and validation with clear pass/fail criteria and documenting failure modes.
• Advancing hardware from prototype to scalable commercial product.

Candidate Requirements

• Master’s or PhD in electronic engineering, mechatronics, embedded systems, or a related discipline completed within the last year.
• Legal authorization to work in New Zealand.
• Experience in PCB design focused on sensor ICs or mixed-signal boards, including schematic and layout.
• Familiarity with embedded systems and sensor integration techniques.
• Methodical approach towards hardware testing and validation procedures.
• Willingness to work on-site in Hawke's Bay for the 6-month contract duration.

Preferred Skills

• Experience with large language models (LLMs) or applied machine learning including model prompting, fine-tuning, or edge deployment.
• Knowledge of edge AI inference optimisation such as quantisation and model-to-hardware matching.
• Skills in designing enclosures for demanding or outdoor industrial settings.
• Background in design for manufacture and early-stage production processes.

Terms and Additional Information

• The role is a funded, full-time 6-month contract position located on-site in Hawke's Bay, with no current plans for extension.