Staff Research Scientist - Foundation & World Models

Job summary

Responsibilities

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…

• 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

What they're looking for

• 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…
• You have driven technically ambitious research at meaningful scale as an individual contributor, whether in an industry research lab, startup, academic environment, or another…
• 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…
• You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical…
• 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