About the company
Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence.
Responsibilities
- Lead and develop a team of engineers and applied scientists focused on cost-efficient specialized models and AI security capabilities
- 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
Requirements
- 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
- Experience taking AI products from 0 to 1 is strongly valued: able to bring structure to early-stage work by scoping clear hypotheses, moving quickly toward signal, and making deliberate decisions about what to pursue, pivot, or stop
- BS/MS/PhD in Machine Learning, Computer Science, Engineering, or related field, or equivalent professional experience