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DoorDash

Runs apps for local food and grocery delivery

Senior Staff Machine Learning Engineer - Ads

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • At least 5 years of experience building, deploying, and scaling machine-learning and artificial-intelligence models for large-scale, user-facing or data-intensive products.
  • Proficiency with AI coding tools such as Claude Code, Codex, or Cursor throughout the software development lifecycle, including designing, generating code, testing, monitoring, and releasing software.
  • A BS, MS, or PhD in Computer Science, Engineering, or a related field, or equivalent practical experience.
  • Deep expertise in one or more of deep learning, large language models, information retrieval, ranking and relevance, recommendation systems, natural language processing, or content understanding.
  • Strong programming skills in Python, Java, or C++, with hands-on experience using machine-learning frameworks such as PyTorch, TensorFlow, or XGBoost.
  • Extensive experience across the machine-learning lifecycle, including data analysis, feature engineering, iterative model development, rigorous offline and online evaluation, and ongoing monitoring and improvement.
  • Ability to apply cutting-edge machine-learning and artificial-intelligence techniques to real-world problems.

Responsibilities

  • Apply state-of-the-art machine-learning and large language model techniques to personalization, query understanding, user understanding, and content understanding.
  • Evaluate machine-learning and large language models through offline analysis and online experimentation, designing metrics and experiments to measure quality, impact, and tradeoffs.
  • Own the full model lifecycle from research to production, including data analysis, model development, evaluation, offline and online A/B testing, and continuous iteration.
  • Partner with product managers, data scientists, and designers to deliver user-facing improvements through AI-driven systems.
  • Assess emerging machine-learning and artificial-intelligence research and translate promising approaches into scalable, production-ready systems.
  • Build and improve machine-learning systems and models that affect financial performance.
  • Partner with engineering, analytics, product, and operations to iterate quickly and move models from prototype to production.
  • Set the long-term technical vision, drive cross-team alignment, and translate research into production systems that meet latency, scale, and reliability constraints.

Desired Qualifications

  • Experience designing and deploying large-language-model systems, including prompt engineering, retrieval-augmented generation architectures, and Generative RecSys.
  • Experience solving large-scale, user-centric and content-centric personalization problems, including user modeling, retrieval, ranking, and relevance.
  • Contributions to the machine-learning community through open-source projects, publications, or applied research in machine learning, natural language processing, information retrieval, or related fields.

About the Company

DoorDash

Runs apps for local food and grocery delivery

DoorDash runs apps that let people order restaurant meals, groceries, and other goods from local businesses, which independent couriers called Dashers bring to their doors. In 2019 it overtook Grubhub to become the largest food delivery service in the United States, and it later expanded abroad by buying Finland's Wolt and Britain's Deliveroo. Merchants pay DoorDash a fee on each order and can buy ads in its apps, while customers pay delivery and service fees or join DashPass, a subscription that cuts those fees. Businesses can also hire Dashers through its Drive service to carry orders placed on their own websites. Four Stanford students founded DoorDash in 2013, and it is based in San Francisco, California.

Company size
10,001+
Company stage
IPO
Headquarters
San Francisco, CA
Founded
2013

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