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.