Staff Research Scientist - Post-Training for Observability Native Models

Job description

As a Staff Research Scientist within Datadog AI Research ( DAIR ), you will drive work on fundamental research in post-training and multimodal observability-native decision models as a hands-on individual contributor. You will advance post-training approaches, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will help 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. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do: • Drive post-training research for multimodal decision models and hybrid decision/generative systems to address ambitious research challenges in observability and security. • Own research problems end to end, from framing the question through experimentation, model development, and evaluation • Design and advance post-training methods and training loops for models operating in complex environments • Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable model training and rigorous measurement • Collaborate with cross-functional teams across Research, Product, and Engineering to translate research advances into scalable Datadog capabilities • Share research findings with external audiences upon reaching significant project milestones. 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 AI agents, decision models, 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 or implementing 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 have operated at a senior technical level and have experience leading, 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