Manager Ii, Engineering - Applied AI (noram)

Datadog Datadog · Enterprise · New York, NY · Leadership

Manager II, Engineering - Applied AI at Datadog. Leads teams building AI foundations and AI-powered features across the Datadog platform, including training ML models, building agents, and shipping natural language interfaces. The role involves setting technical vision, shipping AI products end-to-end, coaching teams, and driving collaboration. Requires experience leading multiple teams and deep technical expertise in areas like LLMs, agentic systems, and ML infrastructure.

What you'd actually do

  1. Lead and grow multiple teams of engineers, applied scientists, and managers working on AI-powered features across the Datadog platform
  2. Set technical vision and strategy for your area in partnership with Product
  3. Ship AI products end-to-end, from data pipelines and model training through evaluation, deployment, and production operations
  4. Coach and develop senior engineers and managers, building a culture of high performance, psychological safety, and clear feedback
  5. Drive collaboration across Applied AI, product, and partner engineering teams to deliver on Datadog's AI priorities

Skills

Required

  • AI
  • machine learning
  • data science
  • leading multiple teams
  • mentoring senior engineers and engineering managers
  • large language models
  • agentic systems
  • anomaly detection
  • time-series modeling
  • NLP
  • retrieval-augmented generation
  • ML infrastructure
  • partnering with Product
  • people management
  • attracting talent
  • developing talent
  • retaining talent

Nice to have

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

What the JD emphasized

  • Proven track record building and shipping ML or AI-powered products in production
  • Experience leading and mentoring multiple teams, including senior engineers and engineering managers
  • Deep technical expertise in one or more areas: large language models, agentic systems, anomaly detection, time-series modeling, NLP, retrieval-augmented generation, or ML infrastructure

Other signals

  • builds AI foundations
  • builds AI-powered features
  • train ML models
  • build agents
  • ship natural language interfaces
  • production AI at real scale
  • large language models
  • agentic systems
  • anomaly detection
  • time-series modeling
  • NLP
  • retrieval-augmented generation
  • ML infrastructure
  • build evaluation and quality practices for AI systems