AI Research Scientist – Datadog AI Research (DAIR)
Conducts cutting-edge research in Generative AI, building foundation models and autonomous agents for cloud observability, SRE, and code repair. Requires PhD in ML or related field, publications at top conferences, and expertise in PyTorch/TensorFlow distributed training.
About the job
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.
Skills
PyTorch, TensorFlow, Deepspeed, Megatron-Lm, Generative AI, Foundation Models, Reinforcement Learning, AI Agents, Distributed Training, CUDA
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