Senior Research Scientist, Optimization Systems

Aalyria

5d•$190,000 - $210,000•Hybrid

About The Position

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.

Requirements

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).
Experience with at least one of: Integer optimisation and associated libraries (e.g. Gurobi, CBC, Google OR tools).
Experience with at least one of: Network science, graph theory, geometric deep learning, or representation learning on dynamic graphs (e.g. clustering, temporal GNNs).
Experience with at least one of: Metaheuristic algorithms.

Nice To Haves

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.

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.