Opportunity Description
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-timeconsidered 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...
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