DoorDash USA
Staff Software Engineer, Machine Learning - Personalization
DoorDash USA, San Francisco, California, United States, 94199
About the TeamCome 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.
About the RoleWe’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).
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 applying ML.
Mentor junior team members, and lead cross-functional pods to create collective impact.
We’re excited about you because you have…
8+ 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
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 non-technical 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.
#J-18808-Ljbffr
About the RoleWe’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).
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 applying ML.
Mentor junior team members, and lead cross-functional pods to create collective impact.
We’re excited about you because you have…
8+ 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
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 non-technical 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.
#J-18808-Ljbffr