Senior Machine Learning Engineer, Rider (multiple Teams)

Uber Uber · Consumer · Seattle, WA +2 · Engineering

Senior Machine Learning Engineer role focused on personalizing the Uber rider experience through ML models for product recommendations and merchandising. The role involves defining and driving ML solutions, providing technical leadership, and improving ML engineering best practices. It requires experience with ML frameworks, data pipelines, and applying ML to real-world problems like recommender systems.

What you'd actually do

  1. Defining and driving ML solutions for key strategic problems in the space of product recommendations and merchandising: help riders find and complete rides with the right products, understanding their ride context and modeling their intent while attending to Uber’s business goals, marketplace conditions and efficiencies.
  2. Provide technical leadership to a passionate, experienced, and diverse engineering team. Manage project priorities, deadlines and deliverables and design, develop, test, deploy and maintain ML solutions. Classification, regression, and multi-task learning are in our toolbox.
  3. Raise the bar of ML engineering by improving best practices, producing exemplary code, documentation, automated tests and thorough & precise monitoring, and applying model debugging & interpretation techniques.
  4. Partner with product owners, data scientists and business teams to translate key insights and business opportunities into technical solutions

Skills

Required

  • Computer Science, Engineering, Mathematics or related field
  • 3+ years of experience in software engineering with an emphasis on data-driven methodologies, deep learning, and online experimentation
  • ML methodologies
  • applying ML, statistics, or optimization techniques to solve large-scale real-world problems
  • ML frameworks (e.g. Tensorflow, Pytorch, or JAX)
  • complex data pipelines
  • Python
  • Spark SQL
  • Presto
  • Java
  • Go

Nice to have

  • 5+ years of experience in software engineering specializing in applied ML methods
  • designing and crafting scalable, reliable, maintainable and reusable ML solutions using deep-learning techniques and statistical methods.
  • deep-learning techniques
  • embeddings
  • transformer architectures
  • 1+ years of experience working in a cross-functional and/or cross-business projects, partnering with Product, Scientists, and cross-org leads to shape the team’s strategies
  • PhD degree in Computer Science, Engineering, Mathematics or related field

What the JD emphasized

  • ML methodologies
  • ML frameworks
  • complex data pipelines
  • deep-learning techniques
  • transformer architectures

Other signals

  • personalizing the booking experience
  • predicting preferences
  • recommending Rides products
  • sequential recommendation systems