What are the responsibilities and job description for the Staff Machine Learning Engineer - Merchant Tax Categorization Service position at DoorDash USA?
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 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 transaction level for DoorDash.
- Lead 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.
- Being exposed to new opportunities where AI/ML can be used as a lever that benefits new business, new markets, and new regions.
You can find out more on our ML blog here
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.
- You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down
- The desire for impact with a growth-minded and collaborative mindset
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