Optimization Engineer, Working Student (m/f/d)

Role overview Improve the mathematical quality and practical performance of an optimization pipeline. As a working student, you will develop methods that transform problems into solver-compatible forms, reduce unnecessary search and make solver outputs usable, while implementing and optimizing the resulting software.

Responsibilities - Design mathematically valid reformulations across problem classes, variable domains, objectives and constraints. - Develop preprocessing that tightens formulations, removes redundant constraints and derives valid new ones. - Improve solver solutions with local search, greedy heuristics and feasibility repair. - Establish why transformations are correct, with mathematical reasoning as well as computational checks. - Implement methods in the pipeline and improve their runtime and memory use.

Requirements - Current enrollment in mathematics, computer science, physics or a related field; later bachelor’s or master’s study is a good fit. - Strong mathematical grounding, especially in combinatorial optimization and linear or integer programming. - Ability to identify subtle errors in reformulations and explain their consequences. - Strong Python skills and enough Rust or C++ to navigate an existing codebase. - Interest in quantum computing, since the optimization work supports quantum solvers. - Availability to work in Munich or remotely from elsewhere in Germany, and openness to feedback.

Nice to have - Experience with QUBO or Ising formulations, Benders decomposition or preprocessing tools such as PaPILO in the SCIP suite.

Benefits and work setup - Flexible scheduling around studies and exams. - A chance to shape an optimization pipeline and work where mathematical methods meet production software. - Munich office, regular team events and a fitness membership.