As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog’s Product and Engineering teams to help translate research advances into tangible benefits for our customers.

Building on our proven track record of delivering AI-powered solutions, including Bits AI, Toto 2.0, and Scout, Datadog AI Research (DAIR) pursues ambitious, high-risk, high-reward research initiatives focused on solving real-world challenges in cloud observability and security.

We are currently focused on two key research areas:

1. Observability Foundation Models – Building state-of-the-art models for advanced forecasting, anomaly detection, and multi-modal telemetry analysis (logs, metrics, traces, etc.). These models will also provide the foundation for our decision models to natively analyze telemetry data.
2. Observability Native Decision Models  – Developing multimodal (logs, traces, metrics, text, images, ...) hybrid decision and reasoning models that are general and efficient enough to be watching your entire system continuously and effectively. Building and training hybrid Decision/Generative systems, e.g., decision models call on agentic models for deeper investigations or support evidence.

What You’ll Do: 

• Conduct cutting-edge research in Generative AI and Machine Learning, aiming to build specialized Foundation Models and AI Agents for observability, site reliability engineering, and code repair
• Leverage large-scale distributed training infrastructure to pre-train and post-train state-of-the-art models on diverse, real-world telemetry data
• Build simulated environments to facilitate on-policy agentic training and evaluation.
• Share research findings with external audiences upon reaching significant project milestones.
• Collaborate with cross-functional teams (e.g., Product, Engineering) to integrate advanced AI capabilities – like multi-modal analysis or automated incident resolution planning – into Datadog’s product ecosystem
• Stay at the forefront of LLMs, Foundation Models, and Generative AI research and engage with the external research community
• Foster a culture of scientific rigor, innovation, and practical impact, e.g., by actively participating in reading groups and mentoring interns

Who You Are: 

• You hold a PhD in Computer Science, Machine Learning, or a related field, with deep expertise in areas like generative modeling, AI agents, decision models or reinforcement learning (or have equivalent experience)
• You possess extensive experience in designing and implementing deep learning models and agents, and have a strong background in distributed training frameworks (e.g., DeepSpeed, Megatron-LM) and ML libraries (PyTorch, TensorFlow)
• You have a proven track record of conducting impactful research in the field with publications at top-tier venues (e.g., NeurIPS, ICLR, ICML, TMLR)
• You're familiar with efficient training, post-training, fine-tuning, and inference techniques for large foundation models
• You excel at explaining complex models and research findings to both technical and non-technical audiences
• You have strong interest in open-science and open-source contributions, including establishing rigorous benchmarks and sharing research with the community

Bonus Points (any of the following):

• You have a demonstrated ability to bridge cutting-edge research and real-world product applications, ideally with an emphasis on large foundation models, generative AI agents, or domain-specific LLM deployments. 
• You’re passionate about pushing the boundaries of AI while maintaining a strong focus on customer impact, scalability, and responsible deployment of new technologies
• You have experience writing production data pipelines and applications
• You have hands-on experience with GPU programming and optimization, including experience in CUDA

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about AI Research and want to grow your skills, we encourage you to apply.

Requisition ID: R12181