Senior Staff Machine Learning Engineer

DoorDash DoorDash · Consumer · San Francisco, CA · 347 Ads Engineering

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.

What you'd actually do

  1. Apply state-of-the-art machine learning and LLM techniques to problems across personalization, query understanding, user and content understanding.
  2. Rigorously evaluate ML and LLM models using a combination of offline analysis and online experimentation, designing metrics and experiments that clearly measure quality, impact, and tradeoffs.
  3. Own the full model lifecycle from research to production, including data analysis, model development, evaluation, offline and online A/B testing, and continuous iteration.
  4. Partner closely with product managers, data scientists, and designers to ensure AI-driven systems deliver meaningful, user-facing improvements.
  5. Stay at the forefront of ML and AI innovation by assessing emerging research and translating promising approaches into scalable, production-ready systems.

Skills

Required

  • Python
  • Java
  • C++
  • PyTorch
  • TensorFlow
  • XGBoost
  • deep learning
  • large language models
  • information retrieval
  • ranking and relevance
  • recommendation systems
  • natural language processing
  • content understanding
  • full ML lifecycle
  • data analysis
  • feature engineering
  • model development
  • evaluation
  • A/B testing
  • monitoring

Nice to have

  • prompt engineering
  • retrieval-augmented generation (RAG)
  • Generative RecSys
  • user modeling
  • retrieval
  • content-centric personalization
  • open-source projects
  • publications
  • applied research

What the JD emphasized

  • lead the technical direction
  • AI-first experiences
  • ranking and relevance systems
  • state-of-the-art techniques
  • large-scale
  • strict latency, scale, and reliability constraints
  • modern AI
  • LLM-powered decisioning
  • full model lifecycle
  • rigorously evaluate
  • continuous iteration
  • cutting-edge ML and AI techniques
  • 5+ years of experience building, deploying, and scaling ML and AI models for large-scale, user-facing or data-intensive products.
  • Deep expertise in one or more of the following areas: deep learning, large language models, information retrieval, ranking and relevance, recommendation systems, natural language processing, or content understanding.
  • Extensive experience across the full ML lifecycle, including data analysis, feature engineering, iterative model development, rigorous offline and online evaluation, and ongoing monitoring and improvement.

Other signals

  • ranking and relevance systems
  • large-scale ML modeling
  • applied engineering
  • LLM techniques