Most Loved Workplace® Certified JobResearch Manager - Foundation & World Models
Assessed by Most Loved Workplace®About the Role
At Datadog, a Most Loved Workplace® certified employer in the Technology space, Observe, Secure, and Optimize Modern Cloud Infrastructure at Scale.
<p>As a Research Manager within Datadog AI Research (DAIR), you will lead a team working on fundamental research in foundation models and world models while remaining deeply engaged in the technical direction of the work. 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 provide technical leadership to a team of researchers and research engineers, mentor and develop highly technical talent, shape research direction, and collaborate with Datadog’s product and engineering teams to translate research advances into products. This role is designed for an exceptional researcher or technical leader who wants to maintain a strong hands-on research identity while developing and leading a high-performing team.</p>
<p><em data-renderer-mark="true">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.</em></p>
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<p><strong>What You'll Do:</strong></p>
<ul>
<li>Lead research in foundation models, world models, and multimodal learning, setting the technical direction for ambitious research programs grounded in observability and security</li>
<li>Guide and mentor researchers and research engineers while remaining closely involved in research strategy, experimentation, model development, and technical problem-solving</li>
<li>Train large-scale multimodal models on diverse telemetry data, including metrics, logs, traces, topology, events, and other non-text modalities</li>
<li>Advance approaches to pre-training, representation learning, world modeling, scaling, and evaluation for models that learn the dynam
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