We are looking for Systems Optimisation Engineer to develop intelligent test frameworks and optimisation algorithms for optical network system. This cross-disciplinary role blends software engineering, algorithm development, and hardware test integration to reduce test time, improve throughput, and enhance performance analysis. You will collaborate closely with hardware test engineers to refine burst-mode test strategies, optimise equalisation parameters, and accelerate product evaluation from R&D through production.

Responsibilities:

• Join the Optical Network Integration team and collaborate closely with other teams.

• Design and implement automated test frameworks for high-speed optical network system, integrating hardware instrumentation (oscilloscopes, BERTs, burst-mode testers).

• Develop metaheuristic and data-driven optimisation algorithms (e.g., genetic algorithms, simulated annealing, swarm optimisation) to reduce test time and improve measurement efficiency.

• Work with hardware engineers to optimise burst-mode test sequences, equalisation settings (CTLE, FFE, DFE), and link tuning strategies.

• Analyse large datasets from validation and production testing to identify performance trends, bottlenecks, and opportunities for improvement.

• Implement adaptive, hardware-aware test routines that adjust dynamically based on device behaviour.

• Support the integration of optimised test flows into high-volume manufacturing environments.

• Maintain scalable, modular software architectures for future test platforms.

Skills & Experience:

• Proficiency in software development for test automation (Python, C++, or C#).

• Experience with metaheuristic optimisation (e.g., GA, simulated annealing, particle swarm).

• Experience with AI/ML techniques (e.g., reinforcement learning, predictive modelling) for test optimisation.

• Collaborative mindset to work closely with hardware engineers and manufacturing teams.

• Familiarity with production test time optimisation in semiconductor or optical device environments.

• Exposure to cloud-based data pipelines for large-scale test data processing.

• Degree in Computer Science, Electrical/Electronic Engineering, Applied Mathematics, or related field.