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Aalyria is a leading aerospace technology company specializing in laser communications and software-defined networking platforms for satellite and airborne mesh networks, including cislunar and deep-space communications. The company, which acquired technology from Google, is revolutionizing the orchestration and management of planetary mesh networks across land, sea, air, and space using any radio or optical spectrum.
As a Senior Research Scientist in Optimization Systems, you will contribute to state-of-the-art algorithms addressing large-scale optimization and resource management challenges in communication networks. This is an applied research role where you'll work closely with software engineers to translate research into practical, production-ready capabilities that operate at the scale and speed required by Aalyria's systems.
Key responsibilities include:
- Specifying, researching, designing, and developing scalable optimization algorithms for complex resource allocation problems
- Applying techniques from machine learning, integer optimization, metaheuristics, and other algorithmic approaches to improve solution quality and computational efficiency
- Collaborating with researchers and engineers to integrate research into commercial product capabilities
- Evaluating new approaches against realistic problem sizes, constraints, and performance requirements
- Publishing at leading international conferences and contributing to patent applications
- Developing and maintaining documentation for novel algorithms
Required qualifications:
- PhD and/or 8+ years of equivalent research experience in computer science, engineering, mathematics, statistics, or related field
- Strong proficiency in Python and Linux environments
- Track record of publishing in leading venues (NeurIPS, ICLR, ICML, INFOCOM, IEEE, SIGCOMM, MobiCom, ICC, GLOBECOM)
- Self-motivated, proactive work style
- Experience with at least one of: reinforcement learning/machine learning with Python libraries (PyTorch, TensorFlow, JAX), integer programming/combinatorial optimization, network optimization, or metaheuristic algorithms
