Senior Software Engineer
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 1030% 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 Europes 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 Youll 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 Job requirements Who You Are Essential Experience Background in Mathematical Optimization through academic or industry experience Ideally experienced with Constraint Programming, though strong expertise in Operations Research, Metaheuristics, or Convex Optimization is equally valuable Proven Python programming skills with production-quality code Track record of solving real-world optimization problems Aptitude to translate business requirements into mathematical models Skills That Matter Experience with optimization solvers (OR-Tools, Choco, Gurobi, CPLEX, etc.) Operations Research techniques and Mixed-Integer Linear Programming Metaheuristics Experience with large-scale distributed optimization API design and developer experience Knowledge of Large Language Models and their potential applications Reinforcement learning Fluency in English, any other languages is a plus Mindset & Approach Pragmatic problem-solving mindset Clear communication of complex concepts to diverse stakeholders Passion for applying mathematical techniques to real-world impact Interest in pushing the boundaries of what's computationally possible Why this role matters You'll be working on optimization problems that directly impact critical European infrastructure and enterprises. Your solutions will optimize billions in economic activity, from coordinating complex logistics operations to scheduling critical resources. This is a unique opportunity to work with cutting-edge optimization techniques while solving problems of unprecedented scale and real-world importance. You'll have the freedom to explore novel approaches, including the integration of AI techniques with traditional optimization methods. What We Offer Impact: Your algorithms will transform operations for Europe's most critical enterprises Challenge: Work on complex constraint satisfaction problems at massive scale Innovation: Freedom to explore novel techniques and AI integration Growth: Shape the technical direction of our optimization capabilities Collaboration: Work closely together with our Brussels team Compensation: Competitive salary with performance bonus and equity Benefits: Comprehensive health insurance, group insurance, meal/eco vouchers, full transportation coverage, internet allowance, and additional Superlinear holidays. Tools: MacBook Pro, cloud compute resources, and optimization solver licenses Team: Collaborate with brilliant minds in AI Ready to solve the unsolvable? If you have expertise in mathematical optimization and are excited about applying it to transform how complex systems operate, we want to hear from you. Join us in building optimization solutions that will power the next generation of intelligent enterprise systems.