System Level Engineer - Digital Twin (m/w/d)

You will develop a standalone simulator and modelling environment that bridges the physical behaviour of integrated photonic circuits to system-level simulations, including interfaces to electronic control and measurement flows.

Activities

This is a hands-on role in a small team: you’ll shape how models are built, validated, and simulated—balancing accuracy, scalability, and speed.

Requirements

Responsibilities:

• Implement a system-level simulator for large scale photonic circuits 
• Define and implement higher-level component abstractions (photonic building blocks, circuit-level composition, and control interfaces) 
• Build and maintain a behavioural modelling workflow, using Verilog-A (or a constrained subset) and/or equivalent higher-level model representations 
• Create verification and regression flows against reference simulations (e.g., PICWave) and/or measurements 
• Support calibration-ready simulation loops (parameter fitting, sweeps, sensitivity-style workflows) to validate and tune abstracted models 
• Collaborate closely with photonic designers and system analysts to turn modelling requirements into robust simulation features 

Application Process

Requirements:

• 3+ years experience building system-level simulations in an industry (or similarly production-oriented) environment
• Strong practical experience with at least one of:
• Circuit simulators (SPICE-class) and/or DAE-based simulation workflows
• Verilog-A / behavioral modeling (writing and validating models) 
• Solid software development skills (designing maintainable code, debugging, test discipline) 
• Ability to work independently in an early-stage environment (ambiguity, iteration, prioritization) 

Nice to have:

• Photonic or RF system modeling experience 
• Experience building or using calibration / optimization pipelines (parameter fitting, automated sweeps, verification harnesses) 
• Experience interfacing simulation with:
• Python-based toolchains
• data pipelines (model parameters, test vectors, results management) 
• Familiarity with constraints that enable scalable simulation (hierarchy, modularity, controlled model semantics).