Senior Engineering Manager - Membership

Uber Uber · Consumer · San Francisco, CA +1 · Engineering

Senior Engineering Manager for Uber One's Membership Growth team, focusing on leading a large organization of engineers (including MLEs) to build and scale personalized upsell and retention engines. The role involves architecting systems that use real-time intent signals and propensity models, driving growth initiatives like incentive targeting and churn prevention, and bridging the gap between ML models and product features. Experience in growth domains, ML team leadership, and incentive optimization is preferred.

What you'd actually do

  1. Lead and scale a multidisciplinary organization of 20+ engineers (Backend, Mobile, MLE and Web) and one reporting Engineering Manager.
  2. Architect systems that leverage real-time intent signals and propensity models to deliver personalized upsells across Uber and UberEats, ensuring we maximize conversion without cannibalizing organic growth.
  3. Formulate and execute the strategy for high-impact growth initiatives, such as global incentive targeting, multi-armed bandit experimentation frameworks, and intelligent churn prevention.
  4. Lead a team of Machine Learning Engineers (MLEs) to translate business objectives into algorithmic formulations, bridging the gap between complex ML models and seamless user-facing product features.
  5. Partner with Product, Data Science, and Ops to define a compelling vision for growth that balances high-velocity experimentation with long-term platform stability.

Skills

Required

  • Analytical Mindset & Experimentation
  • A/B testing
  • data-driven decision-making
  • ambiguous business problems
  • defining success metrics
  • iterating based on data
  • Communication & Stakeholder Management
  • distill complex technical concepts for non-technical stakeholders
  • Execution Excellence
  • delivering complex, cross-team projects
  • company-wide impact
  • Bachelors (or higher) in Computer Science, Engineering, or a related field

Nice to have

  • Management Track Record
  • managing engineering teams
  • managing other Engineering Managers
  • large-scale organizations (20+ people)
  • Technical Industry Experience
  • distributed systems
  • scalable backend architecture
  • Growth Domain Expertise
  • Acquisition
  • Retention
  • LTV (Lifetime Value) optimization
  • Leading Machine Learning Teams
  • applying ML to product optimization and personalization at scale
  • Incentive Optimization
  • Personalization at Scale
  • real-time offer surfacing and ranking
  • Multi-Armed Bandits
  • Operational Leadership
  • high-velocity experimentation
  • 99.99% reliability

What the JD emphasized

  • lead a team of Machine Learning Engineers (MLEs)
  • Leading Machine Learning Teams
  • applying ML to product optimization and personalization at scale
  • Personalization at Scale

Other signals

  • leading MLEs
  • applying ML to product optimization and personalization at scale
  • Personalized Upsell and Retention Engines
  • dynamic, model-driven experiences
  • propensity models
  • multi-armed bandit experimentation frameworks
  • intelligent churn prevention
  • real-time offer surfacing and ranking