<div class="content-intro"><p><img style="display: none; max-width: 100%;" src="https://click.appcast.io/greenhouse-te8/a31.png?ent=34&e=22630&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&utm_ptz=EST&utm_rqt=track" alt="" width="1"></p></div><h2><strong>About the Role</strong></h2> <p>DoorDash is building an AI Research org from the ground up, and we're hiring our founding researchers. This is not a role inside an existing team — it's a role that defines what the team becomes. You'll have an outsized influence on the research agenda, hiring, culture, infrastructure choices, and how research connects to the rest of DoorDash.</p> <p>DoorDash sits on a uniquely valuable substrate for AI research: a real-world, multi-sided marketplace operating at massive scale, with millions of consumers, merchants, and Dashers generating data that no academic lab and few companies can access. We want to build a research org that takes that seriously — one that produces work the broader field cares about, and that fundamentally reshapes how local commerce works.</p> <p>You should apply if you want to do ambitious, publishable research in an environment with the data, compute, and operational reach to actually deploy what you build.</p> <h2><strong>What You'll Do</strong></h2> <ul> <li><strong>Set the research agenda </strong>for one or more areas of DoorDash AI Research, in close collaboration with the founding team and leadership</li> <li><strong>Lead high-impact research projects </strong>end-to-end — from problem framing through publication and, where appropriate, production deployment</li> <li><strong>Help build the team </strong>— interview, recruit, and mentor researchers, engineers, and fellows joining the org</li> <li><strong>Shape the org's culture and operating model </strong>— how we publish, how we collaborate with product teams, how we balance open research with proprietary work</li> <li><strong>Partner across DoorDash </strong>with ML platform, product, and operations teams to identify the highest-leverage research bets and translate findings into real-world impact</li> </ul> <h2><strong>What You'll Have Access To</strong></h2> <ul> <li><strong>Novel proprietary data </strong>at marketplace scale — logistics traces, merchant operations, consumer behavior, real-time supply and demand signals, and longitudinal data unavailable anywhere else</li> <li><strong>Scalable data collection </strong>— ability to design and run structured data collection, leveraging DoorDash’s world-class operational scale, from in-the-wild image and video capture to operational task demonstrations and human-in-the-loop annotation, at a scale and physical-world coverage no other org can match</li> <li><strong>High compute budgets </strong>for training and inference, sized to support frontier-scale experimentation including large-model pre-training and post-training, RL training runs, and large-scale evaluation sweeps</li> <li><strong>Full research infrastructure </strong>— DoorDash's internal RL stack, RL environments built on real operational systems, training and evaluation pipelines, and agent evaluation harnesses, with engineering support to extend them as your research demands</li> <li><strong>Direct access to leadership </strong>— a seat at the table for the decisions that shape the research org, with the autonomy to operate as a principal-level researcher</li> <li><strong>Publication freedom </strong>— we expect and support publication at top venues (NeurIPS, ICML, ICLR, RSS, CoRL, KDD, etc.) with a fast, supportive internal review process</li> <li><strong>Compute and data for external collaborators </strong>— budget to bring in academic collaborators, fellows, and visiting researchers as your agenda requires</li> </ul> <h2><strong>Research Areas</strong></h2> <p>We are broadly interested in researchers across the following areas, though the right candidate may reshape this list:</p> <ul> <li><strong>Agentic systems for logistics and local commerce </strong>— long-horizon planning, tool use, multi-agent coordination, and evaluation methodologies for agents operating in physical-world marketplaces</li> <li><strong>Memory and personalization </strong>— transfer RL, continual learning, harness-based improvements, and systems that adapt to individual consumers, merchants, and Dashers over time without catastrophic forgetting or unsafe drift</li> <li><strong>Foundation models for marketplace dynamics </strong>— forecasting, pricing, matching, and personalization at marketplace scale, including domain-specific pre-training and post-training</li> <li><strong>Evaluation and measurement </strong>— new benchmarks, eval harnesses, and methodologies for ML systems deployed in messy, real-world operational settings</li> <li><strong>Multimodal understanding </strong>— vision, speech, and language applied to merchant catalogs, in-store and on-the-road imagery, and consumer interfaces</li> <li><strong>Robotics and embodied AI for last-mile delivery </strong>— perception, planning, and learning systems for the physical edge of the marketplace</li> </ul> <h2><strong>Who We're Looking For</strong></h2> <ul> <li>A strong research trac