Senior Research Scientist, Optimization Systems

About Aalyria

Aalyria is a leading technology company that supplies laser communications technology and temporospatial software-defined networking platforms to the aerospace industry. With technology acquired from Google, Aalyria is at the forefront of innovation in satellite and airborne mesh networks, as well as cislunar and deep-space communications. We are revolutionizing the orchestration and management of planetary mesh networks using any radio or optical spectrum, any orbit, and any hardware across land, sea, air, and space.

Role Overview

As a Research Scientist, you’ll contribute to state-of-the-art algorithms which advance the most challenging large-scale optimization and resource management problems in communication networks. The successful candidate will join a vibrant and growing research team. This is an applied research role, and you will work with software engineers to integrate research efforts into practical capabilities that can operate at the scale and speed required by Spacetime’s production systems.

Responsibilities

• Specify, research, design and develop scalable optimization algorithms for large, complex resource allocation problems.
• Apply techniques from machine learning, integer optimisation, metaheuristic, or other algorithms to improve solution quality and computational efficiency.
• Collaborate with researchers and engineers to translate research into commercial product capabilities.
• Evaluate new approaches against realistic problem sizes, constraints, and performance requirements.
• Publish at leading international conferences and contribute to patent applications.
• Develop and maintain documentation related to novel algorithms developed by the team.

Required Qualifications

• A PhD and/or 8+ years of equivalent research experience in computer science, engineering, mathematics, statistics, or related field.
• Strong skills in Python and working in Linux environments.
• A track record of publishing in leading venues such as NeurIPS, ICLR/ICML, INFOCOM/IEEE, SIGCOMM, MobiCom, or ICC/GLOBECOM.
• Demonstrated ability of working proactively in a self-motivated way.
• Experience with at least one of:
  • Reinforcement learning/machine learning and associated libraries in Python (e.g. PyTorch, TensorFlow, or JAX).
  • Integer optimisation and associated libraries (e.g. Gurobi, CBC, Google OR tools).
  • Network science, graph theory, geometric deep learning, or representation learning on dynamic graphs (e.g. clustering, temporal GNNs).
  • Metaheuristic algorithms.

Preferred Qualifications

• Experience working in the wireless communication, satellite communications and/or software defined networking space.
• Experience deploying optimization or ML models into production systems with real-time or near-real-time performance constraints.
• Familiarity with satellite orbital mechanics, RF link budgets, or scheduling problems in constrained networks.
• Experience with distributed computing frameworks (e.g., Ray, Dask) for scaling optimization or training workloads.
• Contributions to open-source optimization, ML, or networking libraries.
• Experience mentoring junior researchers or engineers, or leading small technical workstreams.

ITAR/EAR Requirements

This position involves access to export-controlled information. To comply with U.S. government export regulations, applicants must meet one of the following criteria:

• Qualify as a U.S. person, which includes:
  • U.S. citizen or national
  • U.S. lawful permanent resident (green card holder)
  • Refugee under 8 U.S.C. 1157
  • Asylee under 8 U.S.C. 1158
• Be eligible to access export-controlled information without requiring an export authorization.
• Be eligible and reasonably likely to obtain the necessary export authorization from the appropriate U.S. government agency.

The company reserves the right to decline pursuing an export licensing process for legitimate business-related reasons.

Pay

• 190,000 – 210,000 USD per year (Remote – United States)
• 200,000 – 220,000 USD per year (Remote – San Francisco, CA)