Machine Learning Solutions Architect

1 week ago

Seattle, Washington, United States DoorDash Full time

About the Role

We're seeking a skilled Applied Machine Learning expert to leverage our robust data and machine learning infrastructure. In this role, you'll build recommendation systems and implement new AI solutions to expand restaurant selection and drive growth. You'll conceptualize, design, and evaluate A/ML solutions, demonstrating a strong command of production-level machine learning and a passion for collaboration with multi-disciplinary teams to set strategy and execute business growth.

You're excited about this opportunity because you will...

  • Develop production machine learning solutions, central to powering multiple teams including Sales Operation, Product, and Marketing.
  • Partner with engineering and product leaders to shape the product roadmap leveraging AI/ML.
  • Own the modeling life cycle, including feature creation, model development and deployment, experimentation, monitoring, and explainability, and model maintenance.
  • Find new ways to use diverse data sources and modeling techniques, such as NLP, ranking, personalization, image classification, and entity resolution, to provide an AI-driven world-class merchant experience.

We're excited about you because you have...

  • 1+ years of industry experience post PhD or 3+ years of industry experience post graduate degree in developing machine learning models with business impact.
  • Expertise in applied ML for Causal Inference and Recommendation Systems, with classical and deep learning-based approaches. Additional familiarity with experimentation, computer vision, and LLMs.
  • M.S., or PhD. in Statistics, Computer Science, Economics, Math, Operations Research, Physics, or other quantitative fields.
  • Ability to communicate technical details to non-technical stakeholders.
  • Strong machine learning background in Python; experience with Spark, PyTorch, or TensorFlow preferred.
  • Familiarity with Kotlin/Scala.
  • You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress, and then double down.
  • The desire for impact with a growth-minded and collaborative mindset.

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