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Staff Machine Learning Engineer - Merchant Tax Categorization Service

DoorDash USA
California, MO Full Time
POSTED ON 1/15/2025
AVAILABLE BEFORE 3/28/2025

About the Team

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Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop the AI / ML to power DoorDash's growing Merchants.

About the Role

We’re looking for a passionate staff level Applied Machine Learning expert to join our team. In this role, you will utilize our robust data and machine learning infrastructure to develop a comprehensive AI / ML service to create accurate tax categorization for billions of restaurants and non-restaurants items at DoorDash. You will be expected to lead and build a production-level AI / ML solution, collaborate cross-functionally, and push the boundaries of LLM capabilities and lead the AI product strategy for the team to execute.

You’re excited about this opportunity because you will…

  • Lead and develop cutting-edge ML-driven tax catalog solutions, utilizing Generative AI to efficiently manage and organize tax categorization information.
  • Design ML products to solve large scale categorization problems at the transaction level for DoorDash.
  • Collaborate with engineering and product leaders to shape the product roadmap leveraging AI / ML.
  • Own the modeling life cycle end-to-end including feature creation, model development and deployment, experimentation, monitoring and explainability, and model maintenance.
  • Explore new opportunities where AI / ML can be used as a lever that benefits new business, new markets, and new regions.

We’re excited about you because you have…

  • 6 years of industry experience leading and developing advanced machine learning models with business impact, and shipping ML solutions to production.
  • Expertise in applied ML for deep learning, NLP, LLM, and multi-modality models.
  • M.S. or PhD in Statistics, Computer Science, Economics, Math, Physics, or other quantitative fields.
  • Ability to communicate technical details to nontechnical stakeholders.
  • Strong machine learning background in Python; experience with Spark, PyTorch or TensorFlow preferred.
  • Familiarity with Kotlin / Scala.
  • A growth-minded and collaborative mindset with a focus on impact.
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