Manager I, Applied AI - Edge Models

Datadog Datadog · Enterprise · Paris, France · Leadership

Manager for an Applied AI team at Datadog, focusing on building and shipping specialized AI models and AI security capabilities for enterprise customers. The role involves leading engineers and applied scientists, defining technical direction, and owning end-to-end delivery of AI systems from research to production, with a strong emphasis on evaluation, cost-efficiency, and customer impact.

What you'd actually do

  1. Lead and develop a team of engineers and applied scientists focused on cost-efficient specialized models and AI security capabilities
  2. 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
  3. 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
  4. 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
  5. 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

Skills

Required

  • people-focused management experience
  • leading and mentoring engineers
  • deep expertise in LLMs, RAG, semantic search, agentic systems, deep learning, or NLP
  • AI system evaluation methodologies (offline benchmarks and online metrics)
  • product instinct
  • experience taking AI products from 0 to 1

Nice to have

  • BS/MS/PhD in Machine Learning, Computer Science, Engineering, or related field, or equivalent professional experience

What the JD emphasized

  • cost-efficient specialized models
  • AI security capabilities
  • evaluation and iteration practices for AI systems
  • balancing quality, latency, cost, and safety considerations

Other signals

  • building specialized models
  • shipping AI capabilities to customers
  • balancing quality, latency, cost, and safety