Program structure
Track: Research
Reports to: Lead research scientist, EOS Stochastic Models team
Duration: 16–24 weeks, full-time preferred
Primary languages: Python (Num Py, Sci Py, JAX or Py Torch), 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 Git Hub 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 EO...