Staff Machine Learning Engineer - Dashpass

DoorDash DoorDash · Consumer · San Francisco, CA · 341 Executive Engineering

Staff Machine Learning Engineer to design and develop large-scale ML/optimization systems for personalization efforts within the DashPass subscription loyalty program. The role involves contributing to causal inference modeling, incentive optimization frameworks, budget allocation models, and building 0->1 ML systems to improve subscriber outcomes and marketplace health. It requires strong ML fundamentals, production ML system experience, and leadership through influence.

What you'd actually do

  1. Contribute to Causal inference modeling to measure the incremental impact of DashPass Subscriber acquisition and retention strategies.
  2. Incentive optimization frameworks that personalize progressive rewards to improve spend efficiency.
  3. Budget allocation and forecasting models that identify optimal spend across acquisition, referrals, and retention.
  4. Partner closely with Product, Data Science, and Engineering teams to design experiments, model frameworks, and production ML systems that directly impact DashPass subscriber growth metrics.
  5. Build and deploy 0→1 ML systems that improve subscriber outcomes and marketplace health.

Skills

Required

  • M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field.
  • 8+ years of industry experience building production-scale ML systems.
  • Strong understanding of probability theory, statistics, and machine learning fundamentals.
  • Strong programming skills in Python, Java, or C++
  • experience with ML frameworks such as TensorFlow, PyTorch, or XGBoost.
  • Experience in subscriptions growth or marketplace systems is a plus.

Nice to have

  • Interest in building and leading a new team that has broad impact across a wide range of problem spaces to support a critical business line.
  • Proven ability to lead cross-functional initiatives and drive complex technical projects end-to-end.
  • Excellent communication skills — able to explain technical concepts to product, business, and engineering audiences.

What the JD emphasized

  • production-scale ML systems
  • building and leading a new team
  • complex technical projects end-to-end

Other signals

  • personalization
  • optimization
  • causal inference
  • incentive optimization
  • budget allocation
  • forecasting
  • ML systems
  • subscriber growth