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Senior Algorithm Engineer – Optimization & Resource Allocation

אלקטרוניקה וחשמל

Algorithems, אלגוריתמים 
מרכז

על המשרה

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.

דרישות

  • 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.

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