Manager I, Engineering - Applied AI - Frontier Agentic Models

<p>Applied AI is where Datadog's ambitious AI bets get built and shipped (<a href="https://www.datadoghq.com/blog/dash-2026-new-feature-roundup-ai/#search-analyze-and-take-action-across-datadog-faster-with-bits-chat">Bits Chat</a>, <a href="https://updog.ai/">updog</a>). We sit at the intersection of research and product: turning promising capabilities from Datadog AI Research lab and the research community into production systems that reach real customers. The team builds the foundations for agentic systems capable of operating at scale in complex production environments. Current bets span agents that run autonomously at scale, context and memory layers that make those agents more intelligent over time, and tools that help customers build and validate AI-native services in production. The mandate is to move fast from idea to customer impact, and when a product finds its footing, to set it up for growth.<br><br><br>As an Engineering Manager I in Applied AI, you will lead a team of engineers and applied scientists working on one of these challenges. You will define technical direction, run short feedback loops, make deliberate decisions about what to pursue or stop, and work closely with product managers, research teams, and cross-functional partners to ship AI capabilities that matter.<br><br><br><em>At Datadog, we place value in our office culture, the relationships and collaboration it builds and the creativity it brings. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.<br><br><br></em><strong>What You'll Do</strong>&nbsp;</p> <ul> <li>Lead and develop a team of engineers and applied scientists focused on building the foundations for agents operating at scale</li> <li>Work closely with product managers, research teams, and cross-functional partners to shape the team's bets from initial framing through to broader adoption, with a clear definition of success criteria at each stage</li> <li>Own end-to-end delivery of high-quality AI systems, from early research exploration to production-grade reliability, with high standards for operational excellence, system reliability, and technical quality</li> <li>Navigate the unique challenges of shipping AI-powered products: balancing quality, latency, cost, and safety considerations. Drive evaluation and iteration practices for AI systems: define the quality bar and guide the team in building the offline and online evaluation pipelines needed to measure quality and detect drift</li> <li>Contribute to cross-team collaboration and knowledge sharing across the broader AI organization</li> <li>Support career growth for engineers through coaching, feedback, and fostering a culture of experimentation, innovation, and learning. Participa