Machine Learning Engineer - ETA Team

About the Team

Join us in building the world's most reliable on-demand logistics engine for delivery! We are bringing on a talented Machine Learning Engineer to help us develop and improve the ETA models that power DoorDash's three-sided marketplace of consumers, merchants, and dashers. As a fundamental area of investment for DoorDash, ETA has among the coolest problems to solve at scale and creates a major impact on the company and its businesses.

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 optimization ETA models that impact millions of users across our three audiences and tackle our most challenging business problems. 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…

• Build Deep Learning models for next-generation ETA that provide the most accurate, scalable and robust time predictions and enhance the consumer, merchant, and dasher experience.
• Own the modeling life cycle end-to-end, including feature creation, model development and testing, experimentation, monitoring and explainability, and model maintenance. 
• Being exposed to new opportunities where ETA can be used as a lever that benefits new business, new markets, and new regions.
• You can find out more on our ML blog post here.

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

• 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, or other related quantitative fields.
• Strong background in Deep Learning and OSS ML technologies such as Spark, PyTorch, Airflow with hands-on experience in production.
• Demonstrated expertise with programming languages e.g. python and machine learning libraries e.g., Spark MLLib, PyTorch, etc.
• Deep understanding of complex systems such as Marketplaces, and domain knowledge in two or more of the following: Deep Learning, Reinforcement Learning, Operations Research / Optimization, and LLM.
• 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.

The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey

Compensation

The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions.  Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.

In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.