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 partnering across functions
• Impact, you want to ship frontier systems to production and see your work being leveraged by hundreds of millions of consumers
• Excellent written and verbal communication, and a track record of mentoring or developing other researchers

PhD in a relevant field is typical but not required. We weigh demonstrated research output and judgment far more heavily than credentials.

Why Now

DoorDash is at an inflection point: the company has the data, scale, capital, and operational reach to be one of the most important places in the world to do applied AI and foundational research, and we're committing to building the org that lives up to that. Founding researchers will have an outsized impact on what DoorDash AI Research becomes, and on what gets built on top of it.

Notice Regarding Use of AI and Automated Tools: To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Gem.

How it works: Gem assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Gem or other AI-enabled tool in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law.

Data Retention, Privacy & Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our Careers Page.