About the job
As an Engineering Manager I in Applied AI, you will lead a team of engineers and applied scientists building natural language interfaces and conversational AI systems. Your teams will work on challenges spanning LLM-powered query translation, context-aware retrieval systems, agentic architectures, and evaluation frameworks. You will guide technical execution, support team growth, and collaborate across engineering, product, and research to deliver AI experiences that fundamentally change how customers use Datadog.
Responsibilities
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
Qualifications
Minimum
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
Preferred
No preferred qualifications listed.