AI Research Scientist - Datadog AI Research (DAIR)
- 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 agents (described below) to natively analyze telemetry data.
- Site Reliability Engineering (SRE) Autonomous Agents - Creating AI agents to automatically detect, diagnose, and resolve incidents in production environments, pushing the boundaries of multi-step planning, reasoning, and domain-specific knowledge.
- Production Code Repair Agents - Developing agents and models that leverage code, logs, runtime data, and other signals to identify, fix, and even preempt performance issues and security vulnerabilities in production code.
- 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.
- Lead and contribute to research publications, present findings at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and help open-source key model artifacts and benchmarks
- 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
- You hold a PhD in Computer Science, Machine Learning, or a related field, with deep expertise in areas like generative modeling, AI agents, reinforcement learning, or natural language processing (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
- 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
- Competitive global benefits
- New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
- Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris
- Opportunity to attend and present at conferences and meetups
- Intra-departmental mentor and buddy program for in-house networking
- An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
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