Research Science Intern (PhD)

The shape of it

In the posting’s own words

You will frame a problem, run your own experiments, and write up what you find, with compute and data at a scale most academic labs cannot provide. You will collaborate with colleagues working on the same questions.

What it asks for · 7

• Currently enrolled in a PhD program at Carnegie Mellon in machine learning, computer science, statistics, or a related field
• Depth in at least one area relevant to the research above
• Comfort running real experiments — training models, working with GPUs, reading and reimplementing recent papers
• Evidence you can do research: conference or workshop papers, preprints, open-source contributions, or a lab project you can discuss in depth
• Research taste. You can explain why a problem matters and what would change if it were solved.
• Honest empiricism. You report the ablation that didn't work.
• Support from your faculty advisor.

Also a plus

• A long publication record. How you think matters more than how much you've published.
• Observability, monitoring, or SRE background.
• A perfect match to the areas above.

What the job covers

• Own a research project end to end: framing the question, running the experiments, writing it up
• Work directly with a DAIR mentor engaged in the same problem, and stay connected to your advisor and lab
• Publish, and use the work toward your dissertation
• See research reach production, when it works

Degree language

• Currently enrolled in a PhD program at Carnegie Mellon in machine learning, computer science, statistics, or a related field

Tools and skills named

Words the posting leans on

• research11×
• lab7×
• dair4×
• problem4×
• carnegie mellon3×
• experiments3×
• project3×
• area2×
• dair mentor2×
• data2×
• depth2×
• end2×
• experience2×
• faculty advisor2×
• infrastructure2×
• machine learning2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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