Staff Software Engineer, Machine Learning

Found in: Talent US C2 - 1 week ago


Sunnyvale, United States DoorDash Full time

About the Role

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.

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 spacePartner with engineering and product leaders to help shape the product roadmap applying MLMentor junior team members, and lead cross functional pods to create collective impactYou can find out more on our ML blog

We’re excited about you because you have...

8+ years of industry experiencedeveloping machine learning models with business impact, and shipping ML solutions to production.Expertise in applied ML for Causal Inference and Recommendation Systems - both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms & LLMs are a plusMachine learning background in Python; experience with PyTorch or TensorFlow preferred.Familiarity with Kotlin/ScalaAbility to communicate technical details to nontechnical stakeholdersM.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative fieldsYou keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double downDesire for impact with a growth-minded and collaborative mindset
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