DoorDash USA
Staff Machine Learning Engineer - Merchant Tax Categorization Service
DoorDash USA, California, Missouri, United States, 65018
About the Team
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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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.
#J-18808-Ljbffr