Research Science Intern (PhD)

Datadog AI Research (DAIR) is partnering with Carnegie Mellon University to support a small number of PhD students working on open research problems grounded by ongoing efforts at Datadog/DAIR.

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.

The lab and the research:

DAIR is an industrial research lab motivated by practical challenges in observability and software operation: detecting and diagnosing failures, understanding complex production environments, and helping engineers operate software more effectively. The lab focuses on creating specialized foundation models, post-training and evaluating AI agents, and building frontier-scale machine learning systems. By combining fundamental research with Datadog's large-scale, real-world data and infrastructure, the lab develops new AI capabilities and translates them into practical systems with meaningful impact.

Internship projects are shaped with your DAIR mentor and your CMU faculty advisor. You do not need prior experience with observability, monitoring, or infrastructure.

What You'll Do:

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

Who You Are:

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.

Not Required:

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.

Datadog values people from all walks of life. We know not everyone will meet all the above qualifications on day one. That’s okay. If you’re passionate about technology and want to grow your experience, we encourage you to apply.

This job is available in various departments within our company; to conform to US export control regulations, some of these roles may require candidates to be eligible for any required authorizations from the US government.

If possible, please apply using your personal email address instead of your university email address.

Students can sign up for a free Datadog Pro account to learn more about our platform and products.

#LI-Hybrid

Datadog offers a competitive salary for this role and may include additional compensation elements. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, paid time off, and benefits related to traveling and relocation, if eligible.

The reasonably estimated yearly salary for this role at Datadog is:

$110,000—$140,000 USD