Staff Machine Learning Engineer, Personalization

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 modern growth and personalization models that power DoorDash's growing retail and grocery business.

We’re looking for a passionate Applied Machine Learning expert to join our team. As a Staff Machine Learning Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the growth and personalization experiences at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories. You will demonstrate a strong command of production level machine learning,  experience with solving end-user problems, and collaborate well with multi-disciplinary teams.

You will report into the engineering manager on our Personalization team. We expect this role to be hybrid with some time in-office and some time remote (#LI-Hybrid).

• Develop production machine learning solutions to build a world class personalized shopping experience for a diverse and expanding retail space
• Partner with engineering and product leaders to help shape the product roadmap applying ML
• Mentor junior team members, and lead cross functional pods to create collective impact

You can find out more on our ML blog here

• 8+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. 
• Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software
• M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field
• Expertise in applied ML for Causal Inference and Recommendation Systems  -  both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms & LLMs is a plus.  
• Machine learning background in Python; experience with PyTorch or TensorFlow preferred.
• Ability to communicate technical details to nontechnical stakeholders
• 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
• Desire for impact with a growth-minded and collaborative mindset