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. 

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.

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  

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).