Member of Technical Staff, Lead Researcher

About the Role 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. 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. 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. What You'll Do Set the research agenda for one or more areas of DoorDash AI Research, in close collaboration with the founding team and leadership Lead high-impact research projects end-to-end — from problem framing through publication and, where appropriate, production deployment Help build the team — interview, recruit, and mentor researchers, engineers, and fellows joining the org Shape the org's culture and operating model — how we publish, how we collaborate with product teams, how we balance open research with proprietary work Partner across DoorDash with ML platform, product, and operations teams to identify the highest-leverage research bets and translate findings into real-world impact What You'll Have Access To Novel proprietary data at marketplace scale — logistics traces, merchant operations, consumer behavior, real-time supply and demand signals, and longitudinal data unavailable anywhere else Scalable data collection — 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 High compute budgets 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 Full research infrastructure — 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 Direct access to leadership — a seat at the table for the decisions that shape the research org, with the autonomy to operate as a principal-level researcher Publication freedom — we expect and support publication at top venues (NeurIPS, ICML, ICLR, RSS, CoRL, KDD, etc.) with a fast, supportive internal review process Compute and data for external collaborators — budget to bring in academic collaborators, fellows, and visiting researchers as your agenda requires Research Areas We are broadly interested in researchers across the following areas, though the right candidate may reshape this list: Agentic systems for logistics and local commerce — long-horizon planning, tool use, multi-agent coordination, and evaluation methodologies for agents operating in physical-world marketplaces Memory and personalization — 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 Foundation models for marketplace dynamics — forecasting, pricing, matching, and personalization at marketplace scale, including domain-specific pre-training and post-training Evaluation and measurement — new benchmarks, eval harnesses, and methodologies for ML systems deployed in messy, real-world operational settings Multimodal understanding — vision, speech, and language applied to merchant catalogs, in-store and on-the-road imagery, and consumer interfaces Robotics and embodied AI for last-mile delivery — perception, planning, and learning systems for the physical edge of the marketplace Who We're Looking For A strong research track record:  first-author publications at top ML venues, or equivalent demonstrated output (widely-used systems, influential open-source work, or research artifacts adopted at scale) Experience leading ambitious research projects end-to-end, from problem framing through to results that other people built on Taste:  the ability to identify which problems are worth working on, when an approach is exhausted, and when a result is real A builder's instinct:  comfortable working close to real systems and real data, not just on benchmarks Excitement about the founding-team aspects of the role: hiring, agenda-setting, culture-building, and