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Salary: USD 320,000 - 400,000 / annual
Datadog is seeking a Staff Research Scientist to join Datadog AI Research (DAIR) and 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 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 researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products.
Key responsibilities include:
- Drive research in foundation models, world models, and multimodal learning, shaping technical direction of ambitious research programs grounded in observability
- Own research problems end-to-end, from framing questions 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 dynamics of complex distributed systems
- Raise 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
Datadog is the leading observability and security platform for the AI era, providing unified visibility across technology stacks. The company is trusted globally by Fortune 500 companies and high-growth AI leaders.
Requirements:
- PhD in Computer Science, Machine Learning, or related field, or equivalent experience with deep expertise in foundation models, world models, multimodal learning, or generative modeling
- Demonstrated track record of driving technically ambitious research at meaningful scale as an individual contributor in industry research labs, startups, academic environments, or other research settings
- Extensive hands-on experience designing, training, and evaluating large-scale deep learning models (such as large language models); experience with multimodal or non-text data is a strong plus
- Track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements
- Ability to set technical direction through influence rather than authority; experience mentoring other researchers or engineers and elevating quality of work around you
- Commitment to staying deeply hands-on in research for the long term with ability to communicate complex research findings effectively across technical and non-technical audiences
