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 use...