Stochastic Modeling & Simulation Research Intern

Program structure Track: Research Reports to: Lead research scientist, EOS Stochastic Models team Duration: 16–24 weeks, full-time preferred Primary languages: Python (NumPy, SciPy, JAX or PyTorch), some Cython for hot paths Outcome: A reproducible workload generator and a published technical note on tail-risk estimators for AI workloads Compensation: stipend per internal scale; conversion to full-time considered for strong performers. Mentorship: each intern is paired with a senior engineer or researcher who is the technical owner of the area. How to apply: Send • Resume / CV (PDF). • A link to a GitHub profile, portfolio, or representative project. • The role number(s) you are applying for. You can apply for up to two. • The application-prompt response for the role you are most interested in (300–500 words). Applications without the prompt response will be deprioritized it is the single most useful signal we have. About the role The EOS sits on top of a probabilistic model of what AI...