Machine Learning Engineer

Found in: Talent US C2 - 3 weeks ago

Sunnyvale, United States DoorDash Full time

About the Role

As a Machine Learning Engineer, you will have the opportunity to leverage our robust data and machine learning infrastructure to develop inference and ML models that impact millions of users across our three audiences and tackle our most challenging business problems. You will work with other engineers, analysts, and product managers to develop and iterate on models to help us grow our business and provide the best service quality for our customers.

You’re excited about this opportunity because you will…

Build statistical and ML models that run in production to help enhance the consumer experience by reducing cancellations, pickup waiting times, delivery lateness, missing and incorrect items and non fulfilled ordersOwn the modeling life cycle end-to-end including feature creation, model development and prototyping, experimentation, monitoring and explainability, and model maintenance Being exposed to new opportunities where delivery quality can be used as a lever for demand shaping, search ranking, customer segmentation, etcMentor and uplevel a talented team of ML EngineersYou can find out more on our ML blog post

We’re excited about you because…

High-energy and confident — you keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double downYou’re an owner — driven, focused, and quick to take ownership of your workHumble — you’re willing to jump in and you’re open to feedbackAdaptable, resilient, and able to thrive in ambiguity — things change quickly in our fast-paced startup and you’ll need to be able to keep upGrowth-minded — you’re eager to expand your skill set and excited to carve out your career path in a hyper-growth settingDesire for impact — ready to take on a lot of responsibility and work collaboratively with your team

Experience

1+ years of industry experience post PhD or 3+ years of industry experience post graduate degree of developing machine learning models with business impactM.S., or PhD. in Machine Learning, Statistics, Computer Science, Applied Mathematics or other related quantitative fieldsDemonstrated expertise with programming languages, e.g. python, SciKit Learn, Lightgbm, Spark MLLib, PyTorch, TensorFlow, etcDeep understanding of complex systems such as Marketplaces, and domain knowledge in two or more of the following: Machine Learning, Causal Inference, Operations Research, Forecasting and ExperimentationExperience of shipping production-grade ML models and optimization systems, and designing sophisticated experimentation techniquesYou are located or are planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA

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