Staff Research Scientist - Foundation & World Models

As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive research in foundation models and world models as a hands-on individual contributor. You will advance large-scale pre-training and multimodal learning across the diverse signals generated by distributed systems, including metrics, traces, logs, topology, and events. 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 foundation models, world models, and multimodal learning, shaping the technical direction of ambitious research programs grounded in observability Own research problems end to end, from framing the question through experimentation, model development, and evaluation Train large-scale multimodal models on diverse telemetry data, including metrics, logs, traces, topology, events, and other non-text modalities Advance approaches to pre-training, representation learning, world modeling, scaling, and evaluation for models that learn the dynamics of complex distributed systems 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 research advances 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 foundation models, world models, multimodal learning, 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, training, and evaluating large-scale deep learning models (such as large language models), with experience in multimodal or non-text data considered a strong plus 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