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doordashusa

Machine Learning Intern (Masters) - Summer 2027

San Francisco · CA · 发布于 2026-10-06
<div class="content-intro"><p><img style="display: none; max-width: 100%;" src="https://click.appcast.io/greenhouse-te8/a31.png?ent=34&amp;e=22630&amp;t=1701374353806" width="1px"> <img style="display: none; max-width: 100%;" src="https://track.jobadx.com/v1/i.gif?utm_pixel=224e990b-8ff4-4287-8d5d-2ff09647f181&amp;utm_ptz=EST&amp;utm_rqt=track" alt="" width="1"></p></div><h2><strong>About the Team</strong></h2> <p>DoorDash is building the world’s most reliable on-demand logistics engine for delivery! We’re looking for machine learning engineer interns to join our fast-growing engineering team to help us develop a 24x7 global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers.</p> <h2><strong>About the Role</strong></h2> <p>As a Machine Learning Engineer intern at Doordash, you’ll work on tackling new challenges in machine learning and artificial intelligence. You’ll conduct research that can be applied across Doordash engineering teams and engage in external collaborations and mentoring, while also performing research in any of the following areas: Auction, Game theory, Recommender systems, Ranking, AdTech, Computer Vision, Causal Inference, and Big data analytics. We offer a 12-week summer internship program in our San Francisco, Sunnyvale, New York, or Seattle offices.</p> <h2><strong>You’re excited about this opportunity because you will…</strong></h2> <ul> <li>Use cutting-edge research in&nbsp; ML/AI, NLP, RecSys, Ranking, Computer Vision, Causal Inference,&nbsp; Ad Tech, Graph analysis to solve real-world&nbsp; problems across discovery, ads,forecasting, fulfillment&nbsp; and search experiences at Doordash.</li> <li>Contribute and execute on research ideas that can be applied and used to improve product experience at Doordash.</li> <li>Collect, analyze, and synthesize findings from data and use these insights to build relevant ML models.</li> <li>Write clean, efficient, and sustainable code</li> </ul> <h2>We’re excited about you because you…</h2> <ul> <li>Are working towards a Masters degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field and are graduating between Fall 2027 &amp; Summer 2028</li> <li>Have a mastery of at least one systems languages (Java, C++, Python, Kotlin, GoLang) or one ML framework (Tensorflow, Pytorch, MLFlow)</li> <li>Have experience in research and in solving analytical problems</li> <li>Are a strong communicator and team player.</li> <li>Have a passion for applied ML and the Doordash product</li> <li>Ideally have worked on publications in machine learning, AI, data science, data analytics, statistics, or related technical fields</li> </ul> <p>Applications for this position are accepted on an ongoing basis</p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><span style="font-size: 32px;"><strong>Compensation<br></strong></span></p> <p>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.&nbsp; Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.</p> <p>In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.</p> <p>DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance,
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