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 Applied Machine Learning expert to join our team. In this role, you will utilize our robust data and machine learning infrastructure to build new AI solutions to optimize Merchant menus, the most important properties for both Merchant and Consumers. You'll be conceptualizing, designing, and evaluating A/ML solutions. You will be expected to demonstrate a strong command of production-level machine learning, a passion to collaborate with multi-disciplinary teams to lead the strategy and junior team members to execute.
You're excited about this opportunity because you will… Leverage computer vision, NLP, GenAI, entity resolution, and Deep Learning to create the best Consumer-facing menus for Merchants. Lead with engineering and product leaders to build an ML-driven product roadmap. Own the modeling life cycle end-to-end including feature creation, model development and deployment, experimentation, monitoring and explainability, and model maintenance. Develop production machine learning solutions for batch and real-time to provide the world-class merchant experience. We're excited about you because you have… 5+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. M.S. or PhD in Statistics, Computer Science, Economics, Math, Operations Research, Physics, or other quantitative fields. Expertise in applied ML for computer vision, NLP, deep learning, embeddings, and LLMs. Additional familiarity with experimentation and graph-based models! Ability to communicate technical details to non-technical stakeholders. Strong machine learning background in Python; experience with Spark, PyTorch, or TensorFlow. 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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