Senior Algorithm Engineer- Optimization & Resource Allocation

We are looking for a highly analytical Algorithm Engineer to design and optimize advanced resource-allocation solutions for complex, large-scale systems. The role combines mathematical optimization, algorithm development and system-level performance analysis , with a strong emphasis on solving real-world optimization problems under multiple constraints. Key Responsibilities Design, analyze and optimize algorithms for large-scale resource-allocation and scheduling problems. Develop mathematical formulations for complex optimization problems and translate system requirements into algorithmic solutions. Analyze competing objectives and define effective trade-offs between efficiency, performance, coverage and resource utilization. Develop algorithm prototypes and evaluate alternative approaches through simulation. Validate algorithm performance against realistic system scenarios and operational metrics. Optimize algorithms for scalability and computational efficiency. Work closely with algorithm, system and hardware engineering teams to translate mathematical solutions into practical implementations. Strong background in mathematical optimization, including combinatorial and/or convex optimization. Hands-on experience solving complex optimization problems. Strong mathematical, analytical and problem-solving skills. Proficiency in Python and/or MATLAB for algorithm development and prototyping. Ability to translate mathematical and technical specifications into working algorithms. Experience with simulation, performance analysis and algorithm validation. Ability to work effectively across multidisciplinary engineering environments. B.Sc. / M.Sc. / Ph.D. in Electrical Engineering, Computer Science, Applied Mathematics, Operations Research or a related field Advantages Experience in wireless communications, networking or signal processing. Experience with scheduling, resource allocation or network optimization. Experience with large-scale optimization problems. Experience with real-time or performance-constrained systems. Familiarity with optimization solvers and mathematical modeling tools.