DoorDash is actively hiring for 28 AI-related roles. The majority of these positions, 61%, are focused on agents, with application roles making up another significant portion at 29%. Engineering is the primary function for these hires. Frequent tech tags include model serving, agent orchestration, and recommender systems, suggesting a focus on deploying and managing AI models for user-facing applications.
DoorDash currently has 34 active AI-related roles in our index. The most common open titles are: AI Research Fellowship, (Summer and Fall 2026), Associate, Fraud Insights Operations, Business Intelligence Engineer (GTM), In-Store Data & Analytics, Data Analyst, In-Store, Director of Engineering, Logistics. Most positions are in Engineering and Product.
DoorDash's active AI hiring is concentrated in: agents (71%), application (21%), serving infrastructure (6%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
DoorDash is hiring AI talent in: United States (34 roles).
Job postings at DoorDash most frequently reference: agent orchestration, model serving, recommender systems, rag, llm observability.
In the past 30 days, DoorDash has posted 14 new AI-related roles. That is a +100% change versus the prior 30 days (7 → 14).
Currently tracking 12 active AI roles, with 129 new openings in the last 4 weeks. Primary focus: Agent · Engineering. Salary range $102k–$395k (avg $228k).
| Title | Stage | AI score |
|---|---|---|
| AI Research Fellowship, (Summer and Fall 2026) DoorDash is seeking researchers and engineers for a 3-month AI Research Fellowship (extendable to 6 months) to work on applied ML and AI problems in local commerce. Fellows will have access to resources, autonomy, real-world data, and mentorship to pursue ambitious research directions, with the goal of influencing both the field and DoorDash's operations. The program includes dedicated compute, research infrastructure, operational data, mentorship, speaker series, a cohort of peers, publication support, and competitive pay with stipends. Priority research areas include reinforcement learning environments, agentic systems, foundation models for marketplaces, evaluation methods, and multimodal understanding. Candidates should have a strong research track record (PhD candidates/PhDs or equivalent experience) or deep research instincts as an engineer, with the ability to operate independently and communicate effectively. | PretrainAgent | 9 |
| Senior/Staff Deep Reinforcement Learning Engineer Senior/Staff Deep RL Engineer to design, train, and deploy deep reinforcement learning policies for real-time driving decisions in autonomous vehicles. The role involves full lifecycle ownership from problem formulation and reward design to large-scale distributed training and on-vehicle inference, with a focus on JAX end-to-end stack for rapid deployment. | AgentServe | 9 |
| Software Engineer, Machine Learning Infrastructure - Gen AI Software Engineer, Machine Learning Infrastructure - Gen AI role focused on building and scaling the production infrastructure for Generative AI at DoorDash. This includes ownership of core platform surfaces like LLM Gateway, Agent Gateway, evals infrastructure, model serving, batch inference, guardrails, and cost attribution. The role involves designing scalable systems for AI agents, tool orchestration, retrieval, and evaluation workflows, partnering with various teams to enable GenAI-powered products and automation. | AgentServe | 8 |
| Senior Staff Machine Learning Engineer Senior Staff ML Engineer to lead technical direction for AI-first experiences, including ranking and relevance systems for ads and promotions. Will design and build next-generation AI-first ranking systems using state-of-the-art techniques such as sequence modeling, deep learning, and LLMs, spanning query understanding, representation learning, contextual relevance, and multi-objective optimization. Role involves setting technical vision, driving cross-team alignment, and translating research into production systems under strict latency, scale, and reliability constraints. | Ship | 8 |
| Staff Machine Learning Engineer, Fulfillment Planning Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems for DoorDash's fulfillment ecosystem, focusing on real-time decisioning, assignment, and fulfillment estimation. The role involves building 0->1 ML systems, influencing technical direction, setting standards, and mentoring engineers, with a vision towards LLM-inspired foundation models for logistics. | Agent | 8 |
| Director of Engineering, Logistics Director of Engineering for DoorDash's Logistics Org, leading 100+ engineers across Backend, Mobile, ML, and Operations Research. The role focuses on architecting intelligent, real-time systems, optimization engines, and high-throughput AI platforms for fulfillment, dispatching, dynamic pricing, ETAs, and supply-demand balancing. Key responsibilities include defining AI/Platform vision, architecting real-time decision engines, building production-grade ML, scaling infrastructure, and cross-functional leadership. | ServeAgent | 8 |
| Principal Machine Learning Engineer, Ads & Promos Delivery DoorDash is seeking a Principal Machine Learning Engineer to lead the technical direction for AI-first experiences in their Ads & Promos Delivery team. This role involves designing and building next-generation ranking systems using deep learning, LLMs, and sequence modeling, focusing on query understanding, representation learning, and multi-objective optimization. The engineer will own the full ML lifecycle from research to production, ensuring systems operate under strict latency, scale, and reliability constraints, and will contribute to defining how AI reshapes ads relevance in a global marketplace. | AgentServe | 8 |
| Engineering Manager, Merchant, AI/ML Engineering Manager for the Merchant AI/ML team at DoorDash, leading a team to build and deploy AI/ML solutions for the merchant lifecycle. The role involves defining strategy, partnering with product teams, and owning the end-to-end ML system lifecycle, with a focus on generative AI, multimodal learning, and agentic automation to improve merchant success. | ShipAgent | 8 |
| Business Intelligence Engineer (GTM), In-Store Data & Analytics This role focuses on building and shipping production GenAI agents and automations for internal use within a B2B SaaS company. The engineer will translate GTM problems into AI-powered workflows, integrate with existing GTM tools, and contribute to shared AI developer infrastructure. The role requires experience in building production software, deploying LLM applications, and proficiency in Python, SQL, and GCP. | Agent | 7 |
| Manager, Data & Analytics, In-Store Manager of Data & Analytics to lead a team responsible for data products, AI systems, and reporting infrastructure. The role involves people leadership, owning the data roadmap, partnering with stakeholders, and contributing to AI developer infrastructure. Key responsibilities include leading a team, owning the D&A roadmap, translating business problems into technical scope, designing prompts, integrating with systems via APIs, and contributing to AI developer infrastructure. | AgentData | 7 |
| Senior Staff Machine Learning Engineer, Consumer Senior Staff ML Engineer focused on setting personalization strategy for the consumer shopping journey, implementing ML solutions for search relevance, and modernizing recommendation systems using AI. The role involves driving engineering vision, mentoring teams, and partnering on agentic commerce initiatives. Requires deep expertise in deep learning, fine-tuning, and optimizing LLM systems, with a strong command of production ML and experience shipping ML solutions. | AgentPost-train | 7 |
| Software Engineer, Machine Learning - Credit & Refund Optimization Software Engineer, Machine Learning role focused on building and deploying ML systems for credit and refund optimization, personalizing decisions to balance customer experience and operational costs using causal inference and optimization algorithms. | Agent | 7 |
| Senior Software Engineer, Motion Planning – DoorDash Labs Senior Software Engineer focused on motion planning for autonomous delivery systems. The role involves designing, implementing, and deploying behavior and motion planning algorithms, leading root-cause analysis for failures, building evaluation frameworks, and contributing to architecture decisions for planner robustness and efficiency. Requires strong C++ experience and a background in robotics or real-time decision-making systems. | Agent | 7 |
| Machine Learning Engineer, Marketplace Optimization Machine Learning Engineer focused on optimizing DoorDash's Ads Marketplace. This role involves designing, building, and deploying ML models and pipelines for critical functions like pacing, bidding, auction, and targeting. The engineer will collaborate with Data Science and Product teams, improve ML infrastructure, and ensure models are production-ready and scalable, directly impacting financial metrics and marketplace efficiency. | Ship | 7 |
| Staff Machine Learning Engineer - DashPass Staff Machine Learning Engineer to design and develop large-scale ML/optimization systems for personalization efforts within the DashPass subscription loyalty program. The role involves contributing to causal inference modeling, incentive optimization frameworks, budget allocation models, and building 0->1 ML systems to improve subscriber outcomes and marketplace health. It requires strong ML fundamentals, production ML system experience, and leadership through influence. | Agent | 7 |
| Machine Learning Engineer - ETA Team Machine Learning Engineer to develop and improve ETA models for DoorDash's logistics engine. The role involves building deep learning models for time predictions, owning the modeling lifecycle end-to-end, and shipping production-grade ML models and optimization systems. | Serve | 7 |
| Staff Software Engineer, Machine Learning - Personalization Staff Software Engineer, Machine Learning - Personalization at DoorDash. Develops production ML solutions for personalized shopping experiences in retail and grocery delivery. Focuses on Causal Inference and Recommendation Systems, with potential familiarity with MAB algorithms and LLMs. Ships ML solutions to production and partners with product leaders. | ShipAgent | 7 |