AI Research Scientist – Datadog AI Research (DAIR)

What You’ll Do

• Conduct cutting-edge research in Generative AI and Machine Learning to build specialized Foundation Models and AI Agents for observability, SRE, and code repair.
• Leverage large-scale distributed training infrastructure to pre-train and post-train state-of-the-art models on diverse telemetry data.
• Build simulated environments for on-policy agentic training and evaluation.
• Lead and contribute to research publications at top conferences (NeurIPS, ICLR, ICML) and open-source model artifacts and benchmarks.
• Collaborate with cross-functional teams to integrate AI capabilities into Datadog’s products.
• Stay at the forefront of LLMs, Foundation Models, and Generative AI research.
• Foster scientific rigor, innovation, and practical impact through reading groups and mentoring.

Who You Are

• PhD in Computer Science, Machine Learning, or related field with expertise in generative modeling, AI agents, reinforcement learning, or NLP (or equivalent experience).
• Extensive experience designing and implementing deep learning models and agents; strong background in distributed training (DeepSpeed, Megatron-LM) and ML libraries (PyTorch, TensorFlow).
• Proven track record of impactful research with publications at top venues (NeurIPS, ICLR, ICML, TMLR).
• Familiar with efficient training, post-training, fine-tuning, and inference for large foundation models.
• Excel at explaining complex models to technical and non-technical audiences.
• Strong interest in open-science and open-source contributions.

Bonus Points

• Ability to bridge research and real-world product applications, especially foundation models, generative AI agents, or domain-specific LLMs.
• Passion for customer impact, scalability, and responsible AI deployment.
• Experience writing production data pipelines and applications.
• Hands-on GPU programming and optimization, including CUDA.