The opportunity
Applied AI is where Datadog's ambitious AI bets get built and shipped ( Bits Chat , updog ). 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.
What you'll do
Lead and develop a team of engineers and applied scientists focused on: building the foundations for agents operating at scale
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
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
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
Contribute to cross-team collaboration and knowledge sharing across the broader AI organization
Support career growth for engineers through coaching, feedback, and fostering: a culture of experimentation, innovation, and learning. Participate in hiring and help shape the future team as the organization grows
What they're looking for
- A people-focused manager with experience leading and mentoring engineers,: able to develop strong engineering talent in a fast-moving domain
- A technical leader with deep expertise in one or more areas of AI or machine: learning: large language models, retrieval-augmented generation (RAG), semantic search, agentic systems, deep learning, or NLP
- Well-versed in evaluation methodologies for AI systems, both offline benchmarks and online metrics
- A strong product instinct: able to anchor early-stage work in concrete customer problems, define success criteria before writing code, and actively contribute to shaping product direction alongside product and research partners
