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Datadog AI Research (DAIR) is seeking a Research Manager to lead a team focused on fundamental research in foundation models and world models. This role combines technical leadership with hands-on research involvement, ideal for an exceptional researcher or technical leader who wants to maintain a strong research identity while developing and leading a high-performing team.
You will advance large-scale pre-training and multimodal learning across diverse signals generated by distributed systems, including metrics, traces, logs, topology, and events. Your responsibilities include:
- Leading research in foundation models, world models, and multimodal learning, setting technical direction for ambitious research programs grounded in observability and security
- Guiding and mentoring researchers and research engineers while remaining closely involved in research strategy, experimentation, model development, and technical problem-solving
- Training large-scale multimodal models on diverse telemetry data from distributed systems
- Advancing approaches to pre-training, representation learning, world modeling, scaling, and evaluation for models that learn the dynamics of complex distributed systems
- Collaborating with cross-functional teams across Research, Product, and Engineering to translate research advances into scalable Datadog capabilities
- Contributing to research publications, presenting at top-tier conferences (NeurIPS, ICLR, ICML), and helping open-source key model artifacts and benchmarks
Datadog operates as a hybrid workplace, valuing office culture while enabling work-life harmony. The company is the leading observability and security platform for the AI era, trusted globally by Fortune 500 companies and high-growth AI leaders.
QUALIFICATIONS:
- PhD in Computer Science, Machine Learning, or related field, or equivalent experience
- Deep expertise in foundation models, world models, multimodal learning, or generative modeling
- Demonstrated ability to lead technically ambitious research at meaningful scale (industry research lab, startup, academic environment, or similar)
- Extensive hands-on experience designing, training, or evaluating large-scale deep learning models (e.g., large language models); multimodal or non-text data experience 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
- Senior technical level experience with proven ability to lead, mentor, and develop teams of researchers or engineers while remaining deeply hands-on in technical direction and execution
- Ability to communicate complex research findings effectively across technical and non-technical audiences
