The opportunity
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…
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
What they're looking for
- Contribute to research publications, present at top-tier conferences such as: NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks
- 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
