Senior Software Engineer, Machine Learning - Personalization & Growth
DoorDash USA, San Francisco, CA, United States
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 cutting edge search and information retrieval models that power DoorDash's growing retail and grocery business.
About the Role
We’re looking for a passionate Applied Machine Learning expert to join our team. As a Senior Machine Learning Scientist, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the search and personalization experiences at the heart of our fast growing grocery and retail delivery business. In this role, you will leverage 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 be expected to demonstrate a strong command of production level machine learning, a passion for solving end-user problems, and collaborate well with multi-disciplinary teams.
You will report into the engineering manager on our New Verticals Consumer ML team. We expect this role to be hybrid with some time in-office and some time remote (#LI-Hybrid).
You’re excited about this opportunity because you will…
- 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 leveraging ML.
- Mentor junior team members, and lead cross functional pods to generate collective impact.
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, Math, Operations Research, Physics, Economics, or other quantitative field.
- Expertise in applied ML for Search/NLP/IR/Product Knowledge Graph- both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms, LLMs, and causal inference techniques.
- Machine learning background in Python; experience with PyTorch or TensorFlow preferred.
- Familiarity with Kotlin/Scala.
- 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.