Manager I, Engineering - Applied AI - Natural Language & Conversational Interfaces

What You’ll Do

• Lead and develop a team of engineers and applied scientists building NLQ translation systems, conversational agents, or AI-powered interfaces
• Own the delivery of high-quality natural language capabilities, from semantic understanding and contextual retrieval to query generation and agentic reasoning
• Build products that create data flywheels: design systems that capture user intent and feedback to continuously improve model quality and train differentiating capabilities
• Drive evaluation and iteration practices for AI systems, including building offline and online evaluation pipelines to measure quality and detect drift
• Partner with product managers and cross-functional teams to expand AI capabilities across the Datadog platform
• Navigate the unique challenges of shipping LLM-powered products: balancing accuracy, latency, cost, and safety considerations
• Support career growth for engineers through coaching, feedback, and fostering a culture of experimentation, innovation, and learning
• Drive day-to-day execution and promote high standards for operational excellence, system reliability, and technical quality
• Participate in hiring and help shape the future team as the organization grows
• Contribute to cross-team collaboration and knowledge sharing across the broader AI organization

Who You Are

• A technical leader with experience in AI, machine learning, or NLP systems
• Proven experience building and shipping LLM-powered products, conversational AI, or natural language interfaces
• A product builder who thinks beyond features, designing systems that generate data, learn from usage, and compound in value over time
• Experience leading and mentoring engineers; ready to grow into broader leadership responsibilities
• Strong technical expertise in one or more areas: large language models, retrieval-augmented generation (RAG), semantic search, agentic systems, deep learning, or NLP
• Comfortable working with Product to break down ambiguous problems, foster an experimental culture and iterate quickly on AI systems where quality is probabilistic
• Familiarity with evaluation methodologies for AI systems, both offline benchmarks and online metrics
• A people-focused manager able to develop and support strong engineering talent in a fast-moving domain
• BS/MS/PhD in Machine Learning, Computer Science, Engineering, or related field, or equivalent professional experience