Research Science Intern (PhD)

About the role

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 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.

Responsibilities

• 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

Requirements

• 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

Qualifications

• A long publication record is not required
• How you think matters more than how much you've published
• Observability, monitoring, or SRE background is not required
• A perfect match to the areas above is not required

Skills

• Depth in machine learning, computer science, statistics, or related field
• Ability to run experiments with GPUs and reimplement recent papers
• Strong research background (papers, preprints, open-source, lab projects)
• Research taste and ability to articulate problem impact
• Honest empirical reporting

Benefits

• Healthcare
• Paid time off
• Travel and relocation benefits (if eligible)
• Wide range of best‑in‑class, comprehensive and inclusive employee benefits

Pay

The reasonably estimated yearly salary for this role at Datadog is: $110,000—$140,000 USD.

Applicants should be eligible for any required US government authorizations; apply with a personal email address; a free Datadog Pro account is available to learn about the platform.