Network Science and Network Modeling Machine Learning Expert

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

Required qualifications - 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

Employment type: Contract Time commitment: 10 hours/week Location: Remote