Staff Research Scientist - Post-Training for Agents

As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive research in post-training and autonomous agents as a hands-on individual contributor. You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will set the technical direction for ambitious research programs, raise the technical bar for the researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products.

What You'll Do:

Drive research in agent post-training and reinforcement learning, shaping the technical direction of ambitious research programs grounded in observability and security
Own research problems end to end, from framing the question through experimentation, model development, and evaluation
Design and advance reinforcement learning approaches, post-training methods, and training loops for agents operating in complex environments
Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement
Raise the technical bar across the team by reviewing research directions, mentoring researchers and research engineers, and setting standards for experimental rigor
Collaborate with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities
Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks

Who You Are:

You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling
You have driven technically ambitious research at meaningful scale as an individual contributor, whether in an industry research lab, startup, academic environment, or another research setting
You have extensive hands-on experience designing and implementing reinforcement learning systems, agent training methods, simulated environments, synthetic data pipelines, or evaluation frameworks
You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements
You set technical direction through influence rather than authority, and you have mentored other researchers or engineers and elevated the quality of work around you
You want to stay deeply hands-on in research for the long term, and you can communicate complex research findings effectively across technical and non-technical audiences