Senior Scientific Software Engineer - Simulation and Machine Learning

Location:

Remote within Europe, with regular presence in Bremen

Direct Reports:

None; supervision of students and interns

Working Model:

Remote / hybrid. Regular time on-site in Bremen.

Mission

Build the simulation and model-learning engine at the centre of Constructor's quantum software. The work is numerics-heavy: differentiable simulation of physical systems, inference of model parameters from experimental data, and optimisation at scale

sits between physics and engineering and requires genuine depth in both

Pillar 1 Simulation Engine

Success Metrics and KPIs: simulation accuracy against measurement, runtime performance, API stability.

Pillar 2 Model Learning and Optimisation

Success Metrics and KPIs: benchmark results, methods adopted by the research group, publications.

Pillar 3 Research Collaboration

Accountability: research collaborations initiated and sustained; students productive

• Build and maintain high-performance simulation code for open and closed quantum systems.
• Own the differentiable simulation and automatic differentiation layer.
• Drive performance. This code runs inside optimisation loops and speed decides what is possible.
• Maintain and extend the Julia core and its Python interface.
• Implement parameter inference and model learning from experimental data.
• Work on neural-network approaches to PDE solving and equation discovery.
• Benchmark against established academic tools and publish the comparison.
• Work with Constructor University faculty on joint research and grant applications.
• Supervise graduate students and interns working on the engine.
• Contribute to course material on numerical simulation and machine learning for physics.

Experience

Skills & Competencies

Education & Languages

What This Role Is (and Is Not)

This role IS

This role is NOT

• PhD or equivalent in physics, applied mathematics or computational science.
• Strong scientific computing in Julia, Python, JAX or comparable.
• Real numerical depth: ODE solvers, optimisation, stochastic processes, automatic differentiation.
• Published work, or code that others rely on.
• Able to take a method from a paper and make it fast, tested and usable.
• Comfortable owning code quality, not only correctness.
• Interest in supervising students alongside the engineering.
• Clear technical writing.
• PhD in physics, applied mathematics or computational science.
• Fluent English.
• A research-grade engineering role with a product at the end of it.
• Central to the group's core technical asset.
• A role with a route into teaching and supervision if you want it.
• A pure research position without delivery.
• A general software engineering role.
• A role where someone else owns the numerics.