DoorDash USA
Machine Learning Engineer, Forecast Platform
DoorDash USA, San Francisco, California, United States, 94199
About the TeamCome help us build the world's most reliable on-demand logistics engine for delivery! We are bringing on a talented Machine Learning Engineer to help us improve the operational efficiency of DoorDash's three-sided marketplace of consumers, merchants, and dashers. The Forecasting Platform provides a unique opportunity to have a broad impact on the business ensuring we can quickly respond to a rapidly changing operational environment. The team supports projects across all of our major pillars (e.g. supply/demand, auto-scaling infrastructure, support optimization) as well as the development of an internal decision platform for time series.
About the RoleAs a Machine Learning Engineer, you will have the opportunity to leverage our robust data and machine learning infrastructure to develop 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 time-dependent statistical and ML models that solve product needs across our verticals - e.g. matching supply/demand, predicting support needs, providing insights to our internal platforms (ads, experiments, merchant), auto-scale infrastructure and optimize business processes such as promotions.
Own the modeling life cycle end-to-end including feature creation, model development and prototyping, experimentation, monitoring and explainability, and model maintenance.
Contribute to the development of our in-house Forecasting Self-Service Platform and proprietary Forecasting Repository to uplevel our capabilities.
Research new tools within the Forecasting space (e.g. TimeGPT, LLM extensions).
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 down.
You’re an owner — driven, focused, and quick to take ownership of your work.
Humble — you’re willing to jump in and you’re open to feedback.
Adaptable, resilient, and able to thrive in ambiguity — things change quickly in our fast-paced startup and you’ll need to be able to keep up!
Growth-minded — you’re eager to expand your skill set and excited to carve out your career path in a hyper-growth setting.
Desire for impact — ready to take on a lot of responsibility and work collaboratively with your team.
Experience
M.S. and 3+ or PhD. and 1+ year(s) experience of developing advanced statistical and machine learning models in production.
Demonstrated expertise with object-oriented programming and ML Libraries, e.g. python, SciKit Learn, Lightgbm, Spark MLLib, PyTorch, TensorFlow, etc.
Deep understanding of complex systems such as Marketplaces, and domain knowledge in two or more of the following: Machine Learning, Causal Inference, Operations Research, Forecasting.
Experience of shipping production-grade ML models and optimization systems.
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About the RoleAs a Machine Learning Engineer, you will have the opportunity to leverage our robust data and machine learning infrastructure to develop 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 time-dependent statistical and ML models that solve product needs across our verticals - e.g. matching supply/demand, predicting support needs, providing insights to our internal platforms (ads, experiments, merchant), auto-scale infrastructure and optimize business processes such as promotions.
Own the modeling life cycle end-to-end including feature creation, model development and prototyping, experimentation, monitoring and explainability, and model maintenance.
Contribute to the development of our in-house Forecasting Self-Service Platform and proprietary Forecasting Repository to uplevel our capabilities.
Research new tools within the Forecasting space (e.g. TimeGPT, LLM extensions).
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 down.
You’re an owner — driven, focused, and quick to take ownership of your work.
Humble — you’re willing to jump in and you’re open to feedback.
Adaptable, resilient, and able to thrive in ambiguity — things change quickly in our fast-paced startup and you’ll need to be able to keep up!
Growth-minded — you’re eager to expand your skill set and excited to carve out your career path in a hyper-growth setting.
Desire for impact — ready to take on a lot of responsibility and work collaboratively with your team.
Experience
M.S. and 3+ or PhD. and 1+ year(s) experience of developing advanced statistical and machine learning models in production.
Demonstrated expertise with object-oriented programming and ML Libraries, e.g. python, SciKit Learn, Lightgbm, Spark MLLib, PyTorch, TensorFlow, etc.
Deep understanding of complex systems such as Marketplaces, and domain knowledge in two or more of the following: Machine Learning, Causal Inference, Operations Research, Forecasting.
Experience of shipping production-grade ML models and optimization systems.
#J-18808-Ljbffr