The opportunity
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 translate research advances into products.
What you'll do
World Models for Observability -: Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents.
Trained Agents for Observability -: Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost.
Conduct research in generative AI and machine learning, building specialized: foundation models and trained agents for observability
Train multimodal models on large-scale, diverse telemetry data (metrics,: logs, traces, topology, events) using distributed training infrastructure
Design and build simulated environments and RL training loops for on-policy agent training and evaluation
Collaborate with cross-functional teams (Product, Engineering) to integrate: capabilities like multimodal world modeling and autonomous agents into Datadog's products
What they're looking for
- Stay at the forefront of foundation models, world models, and RL-based agent research
- Contribute to research publications, present at top-tier conferences (e.g.,: NeurIPS, ICLR, ICML), and help open-source key model artifacts and benchmarks
- You hold a PhD in Computer Science, Machine Learning, or a related field,: with deep expertise in areas like generative modeling, world models, AI agents, reinforcement learning, or multimodal learning (or have equivalent experience)
- You have extensive experience designing and implementing deep learning models: and agents, with a strong background in distributed training frameworks (e.g., DeepSpeed, Megatron-LM) and ML libraries (PyTorch)
