AI Research Engineer - Datadog AI Research (DAIR)

<p>As a Research Engineer on our team, you will partner with Research Scientists to turn research ideas into working systems, building the data, tooling, and infrastructure that enable rapid iteration, trustworthy evaluation, and a smooth path from prototype to production.</p> <p data-renderer-start-pos="4616">&nbsp;</p> <p>Building on our track record of AI-powered solutions (e.g., <a href="https://www.datadoghq.com/blog/bits-ai-sre/">Bits AI</a>, <a href="https://www.datadoghq.com/blog/engineering/self-optimizing-system/">Bits Evolve</a>, and our <a href="https://www.datadoghq.com/blog/ai/toto-boom-unleashed/">time series foundation model</a>), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security.</p> <p data-renderer-start-pos="4616">&nbsp;</p> <p>We are focused on two research areas:</p> <p data-renderer-start-pos="4616">&nbsp;</p> <ol> <li><strong>World Models for Observability</strong> -- 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.</li> <li><strong>Trained Agents for Observability</strong>-- 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.</li> </ol> <p data-renderer-start-pos="4616">&nbsp;</p> <p><strong>What You'll Do:</strong></p> <p data-renderer-start-pos="4616">&nbsp;</p> <ul> <li>Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling</li> <li>Implement models, run experiments at scale, and profile for reliability, performance, and cost</li> <li>Build simulation environments and replay infrastructure for agent training and evaluation</li> <li>Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery</li> <li>Establish rigorous automated benchmarks and regression tests for world model predictions, agent performance, and simulation fidelity</li> <li>Collaborate with Research Scientists, Product, and Engineering to integrate capabilities into Data