AI Research Scientist - Datadog AI Research (DAIR) Ajouter aux favoris
- 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
- 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)
- You have a track record of impactful publications at top-tier venues (e.g., NeurIPS, ICLR, ICML, TMLR)
- You are familiar with efficient training, post-training, and inference techniques for large foundation models
- You can explain complex models and research findings to both technical and non-technical audiences
- Experience bridging research and real-world product applications, especially with large foundation models, world models, or RL-trained agents
- Passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment
- Experience writing production data pipelines and applications
- Hands-on experience with GPU programming and optimization, including 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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