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.