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. Responsibilities Build deep learning models for next-generation ETA that provide 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. Be exposed to new opportunities where ETA can be used as a lever that benefits new business, new markets, and new regions. Qualifications 1+ years of industry experience post PhD or 3+ years post graduate degree in developing advanced machine learning models with business impact. Proficiency in using AI coding tools (Claude Code, Codex, Cursor) throughout the software development lifecycle. M.S. or PhD. in Computer Science, Statistics, or related quantitative fields. Strong background in deep learning and OSS ML technologies such as Spark, PyTorch, Airflow with hands‑on production experience. Demonstrated expertise with programming languages like python and machine learning libraries e.g., Spark MLLib, PyTorch. Deep understanding of complex systems such as marketplaces and domain knowledge in two or more of Deep Learning, Reinforcement Learning, Operations Research/Optimization, and LLM. Experience shipping production‑grade ML models and optimization systems, and designing sophisticated experimentation techniques. Location: San Francisco, CA, Sunnyvale, CA, or Seattle, WA. Compensation Starting base salary within the listed range, localized by location. Also includes equity grants. Benefits 401(k) plan with employer matching, 16 weeks paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws, medical, dental, vision, 11 paid holidays, disability and basic life insurance, family‑forming assistance, mental health program. Equal Opportunity Employment Statement of Non‑Discrimination: In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status. Above and beyond discrimination and harassment based on protected categories, we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping). Whether blatant or hidden, barriers to success have no place at DoorDash. We value a diverse workforce and strongly encourage application of all eligible candidates. #J-18808-Ljbffr Fairygodboss