Network Science and Network Modeling Machine Learning Expert

This is a remote, project-based role for machine learning researchers and engineers with deep expertise in network science and graph-based modeling. You will complete tasks at the intersection of ML and network analysis — including model development, graph representation learning, and research tasks applied to real-world complex networks spanning social, biological, infrastructure, and information systems. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge network ML research, and a strong addition to your research portfolio.

Responsibilities

• Apply machine learning techniques to complex network problems including community detection, link prediction, network generation, and dynamic network modeling
• Design and evaluate graph neural network architectures tailored to large-scale, heterogeneous, or temporal network data
• Develop generative and predictive models of network structure, diffusion processes, and cascading phenomena
• Conduct rigorous benchmarking of network ML models across diverse real-world graph datasets and tasks
• Conduct rigorous benchmarking of network ML models across diverse real-world graph datasets and tasks

Requirements

• Published researcher with at least one first-author publication in a peer-reviewed venue (e.g., NeurIPS, ICML, ICLR, WWW, KDD, or equivalent)
• Master's or PhD in Computer Science, Applied Mathematics, Physics, Statistics, or a related quantitative field
• Demonstrated expertise in both machine learning and network science (e.g., graph theory, complex systems, network dynamics, or graph representation learning)
• Strong problem-solving skills and ability to work independently on technical and research tasks

Preferred Qualifications

• Hands-on experience with graph ML frameworks and libraries (e.g., PyTorch Geometric, DGL, NetworkX, or similar)
• Familiarity with generative graph models (e.g., GraphRNN, GRAN, GraphVAE, or diffusion-based graph generation)
• Experience with large-scale real-world network datasets (e.g., social networks, citation graphs, biological interaction networks)
• Background in TA'ing or teaching network science, graph theory, or machine learning courses

Why Apply

• Flexible Time Commitment – Work on your schedule while tackling meaningful research challenges
• Startup Exposure – Work directly with an early-stage Y Combinator-backed company, gaining hands-on experience that sets you apart
• Exceptional Pay – Project-based pay ranges from $150–$200/hour
• Portfolio Building – Gain experience applying ML to frontier network science and graph modeling problems
• Professional Growth – Sharpen your skills on varied, challenging real-world network datasets and models