DoorDash

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.

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.

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.

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.

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.

Associate Manager for Sales Strategy & Operations focused on DoorDash's Digital Ordering platform. This role involves translating high-level objectives into actionable sales goals, establishing operational governance, optimizing sales funnels, and leveraging AI to maximize sales productivity and outreach effectiveness. The candidate will architect AI-driven solutions from process discovery to production deployment and build AI tooling to automate manual work and accelerate decision-making.

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.

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.

Engineering Manager to lead a team focused on Search Quality, leveraging ML and LLM approaches for query understanding, retrieval, and multi-stage ranking in a consumer-facing product.

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.

Machine Learning Engineer focused on Computer Vision for DoorDash's grocery and retail delivery business. The role involves developing, implementing, and validating ML solutions for consumer vision problems like entity recognition, object detection, and classification, aiming to improve product knowledge graph accuracy, inventory information, and Dasher efficiency. Requires production-level ML experience and collaboration with cross-functional teams.

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.

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.

Software Engineer, Machine Learning - Fraud at DoorDash. Develops and ships production ML solutions for fraud detection and mitigation at scale. Requires experience with deep learning frameworks and familiarity with multi-task learning, LLMs, and anomaly detection.