Senior Software Engineer - Optimization
The opportunity
Enterprise productivity across Europe is under pressure. As complexity increases through geopolitical shifts, labour constraints, and tighter operational interdependencies, many organisations observe that AI and optimization efforts improve local efficiency but fail to deliver structural, enterprise-level gains.
At Superlinear, we work on this gap through enterprise orchestration: enabling better system-level decision-making across people, processes, and machines in environments where siloed optimization no longer works.
In our earliest pilots with leading European enterprises, we observe that moving from local optimization to coordinated, enterprise-wide decisions can unlock significant latent capacity. Productivity gains in the range of 10–30% are achievable by reducing systemic friction rather than adding infrastructure.
The 500 largest European enterprises represent roughly €14 trillion in economic output. Enabling even a fraction of them to structurally improve productivity would have impact beyond individual companies.
This is a long-term effort that requires rigor, restraint, and people who care deeply about their craft, take responsibility for outcomes, and are comfortable working on complex, high-stakes systems. This role offers the opportunity to contribute meaningfully to the foundations of enterprise orchestration in Europe’s most critical organisations.
That's why we are hiring a Senior Software Engineer - Optimization.
The role
As our Software Engineer specialized in Mathematical Optimization, you'll be building the mathematical engines that solve large-scale industrial optimization problems. You'll work on challenges where optimal solutions can drive millions in economic value.
What you’ll build
Optimization Solutions
Model complex real-world problems as constraint programming problems
Determine the model scope to maximize impact while guaranteeing tractability
Decompose large-scale problems to achieve tractability
Design hard and soft constraints to achieve real-world feasibility
Design (lexicographic) objectives to achieve maximal real-world impact
Implement multi-objective optimization with Pareto frontier computation
Natural Languages Interfaces
Design Python APIs to make constraint programming accessible to developers
Design Python APIs to enable Large Language Models to implement and modify optimization models
Design Natural Language Interfaces that enable business users to efficiently mitigate operational disruptions
Design Natural Language Interfaces that enable business users to simulate and compare different scenarios
Design Natural Language Interfaces that provide intuitive explanations for decision proposals
Scalable Solver Algorithms
Design compilers that translate high-level optimization problem representations into efficient constraint programs
Design (meta-)algorithms that prioritize finding good feasible solutions quickly
Apply Reinforcement Learning to accelerate optimization algorithms
Design distributed optimization solvers wherein multiple strategies collaborate to find good feasible solutions
Robust Software
Develop robust and maintainable Python packages and APIs for optimization
Develop benchmarks and evaluate software performance on those benchmarks
Build comprehensive test suites that validate correctness and robustness
Implement monitoring and observability for optimization systems
