About the Team

Come 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 build the Dasher Acquisition, Mobilization, and Pay systems that power DoorDash’s three-sided marketplace of Consumers, Merchants, and Dashers. The Dasher Supply team works to ensure that Doordash is a profitable and reliable way for Dashers to earn money and that the right number of dashers are active at every moment for successful delivery experiences for Consumers and Merchants.

About the Role

As a Machine Learning Engineer, you will work with our robust data and machine learning infrastructure to deploy MLmodels and optimization programs. Tackling Doordash’s most challenging business problems, our models power spend allocation across Dasher Acquisition (paid media marketing), Dasher Mobilization (proactive and reactive incentives) and Dasher Pay (base and bonus pay)  . You will work with other data scientists, engineers, and product managers to develop and iterate on models to help us grow our business and provide better service quality for our customers.

You’re excited about this opportunity because you will…

• Use Causal ML and Optimization techniques to automate spend allocation across Dasher supply levers to ensure our roads are well supplied for a fantastic Dasher and Consumer experience.
• Build ML models to better forecast Dasher actions in high dimensional contexts, which will serve as core inputs to our offline and online supply management systems.
• Have end-to-end ownership of model ideation, development, testing, deployment, and maintenance.  
• Get a chance to platformize existing supply levers across new business verticals and geographies.
• Be able to measure your business impact through rapid experimentation, making complex tradeoffs to balance different sides of the marketplace.
• Work on complex systems, like those described in this blog post written by your future teammates.

We’re excited about you because you’re…

• 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
• 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
• Impact oriented — 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 advanced machine learning models with business impact.
• M.S., or PhD. in Computer Science, Statistics, Operations Research or other related quantitative fields.
• Strong background in machine learning and OSS ML technologies such as Spark, PyTorch, Airflow with hands-on experience in production.
• Demonstrated expertise with programming languages and machine learning libraries e.g. LightGBM, Spark MLLib, PyTorch, etc.
• Deep understanding of complex systems such as Marketplaces, and domain knowledge in two or more of the following: Machine Learning, ML Ops, Causal Inference, and Operations Research.
• Experience of shipping production-grade ML models and optimization systems, and designing sophisticated experimentation techniques.
• You are located or are planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA.

Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only

We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.

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