Reinforcement Learning & Optimization Intern

Program structure Track: Research engineering Reports to: Staff research engineer, EOS Intelligence Plane team Duration: 20–24 weeks, full-time preferred Primary languages: Python (PyTorch or JAX), familiarity with Stable Baselines / CleanRL / TorchRL Outcome: A trained, sim-validated routing policy that demonstrably improves utility- per-dollar over the production baseline 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 intelligence p...