Machine Learning Engineer - Eta Team

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

Machine Learning Engineer to develop and improve ETA models for DoorDash's logistics engine. The role involves building deep learning models for time predictions, owning the modeling lifecycle end-to-end, and shipping production-grade ML models and optimization systems.

What you'd actually do

  1. Build Deep Learning models for next-generation ETA that provide the most accurate, scalable and robust time predictions and enhance the consumer, merchant, and dasher experience.
  2. Own the modeling life cycle end-to-end, including feature creation, model development and testing, experimentation, monitoring and explainability, and model maintenance.
  3. Being exposed to new opportunities where ETA can be used as a lever that benefits new business, new markets, and new regions.

Skills

Required

  • 1+ years of industry experience post PhD or 3+ years of industry experience post graduate degree of developing advanced machine learning models with business impact.
  • M.S., or PhD. in Computer Science, Statistics, or other related quantitative fields.
  • Strong background in Deep Learning and OSS ML technologies such as Spark, PyTorch, Airflow with hands-on experience in production.
  • Demonstrated expertise with programming languages e.g. python and machine learning libraries e.g., Spark MLLib, PyTorch, etc.
  • Deep understanding of complex systems such as Marketplaces, and domain knowledge in two or more of the following: Deep Learning, Reinforcement Learning, Operations Research / Optimization, and LLM.
  • Experience of shipping production-grade ML models and optimization systems, and designing sophisticated experimentation techniques.

What the JD emphasized

  • shipping production-grade ML models
  • Deep Learning
  • production

Other signals

  • develop inference and optimization ETA models
  • impact millions of users
  • ship production-grade ML models