AI Research Engineer - Datadog AI Research (DAIR)

About the role

Datadog AI Research (DAIR) is looking for a Research Engineer in Paris to partner with Research Scientists and turn research ideas into working systems. The team builds the data, tooling and infrastructure that enable rapid iteration, trustworthy evaluation and a smooth path from prototype to production, focusing on high-risk, high-reward problems in cloud observability and security.

The two research areas are World Models for Observability (multimodal foundation models for forecasting, anomaly detection, root cause analysis and agent planning) and Trained Agents for Observability (autonomous agents for incident response, code repair, security response and infrastructure optimization).

Key responsibilities

• Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks and internal tooling
• Implement models, run experiments at scale and profile for reliability, performance and cost
• Build simulation environments and replay infrastructure for agent training and evaluation
• Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling and failure recovery
• Set up automated benchmarks and regression tests for world model predictions, agent performance and simulation fidelity
• Work with Research Scientists, Product and Engineering to integrate capabilities into Datadog products and harden prototypes into reliable services
• Contribute to research publications at top-tier conferences (e.g. NeurIPS, ICLR, ICML) and deliver high-quality code, documentation and open-source artifacts

Required profile

• Depth in distributed computing, RL infra and ML systems for training and inference at scale; experience with Ray, Slurm or similar is a plus
• Practical experience implementing and operating ML training and inference systems, including containerization, orchestration and GPU acceleration
• Experience with large-scale model training and fine-tuning, including SFT, RLVR, RLHF and efficient inference (quantization, speculative decoding)
• Able to explain design and performance trade-offs to technical and non-technical audiences
• Experience supporting or contributing to research publications

Required skills

• Python, with familiarity with a systems language such as Rust, C++ or Go
• Modern cloud and data infrastructure
• ML frameworks such as PyTorch or JAX
• Large-scale training frameworks such as Megatron-LM, DeepSpeed, SkyRL, VeRL or TorchTitan

What we offer

• Competitive global benefits
• New hire stock equity (RSUs) and an employee stock purchase plan (ESPP)
• Close collaboration with colleagues in the New York City and Paris offices
• Opportunities to attend and present at conferences and meetups
• Intra-departmental mentor and buddy program
• Inclusive culture and access to employee resource groups