Machine Learning Engineer II

Uber Uber · Consumer · New York, NY +2 · Engineering

Machine Learning Engineer II at UberEats Feed, focusing on building and productionizing state-of-the-art recommendation models and large-scale ML systems. The role involves improving model quality, serving foundations, and data foundations for the UberEats Feed, which is the front door for users and merchants. Requires expertise in deep learning, recommendation systems, or optimization algorithms, and experience with ML frameworks and productionizing ML systems.

What you'd actually do

  1. Innovate and productionize start-of-the-art recommendation models, and customize for Uber’s use cases.
  2. Design and build the end-to-end large-scale ML systems to power the HomeFeed Recommendation.
  3. Improve the Feed Model ML Quality, Model Serving foundation and the Data foundation.
  4. Collaborate with cross-functional and cross-team stakeholders.

Skills

Required

  • PhD in relevant fields (CS, EE, Math, Stats, etc.) with recommendation system research experiences or 2 years minimum of industry experience with a strong focus on machine learning and recommendation systems.
  • Expertise in deep learning, recommendation systems, or optimization algorithms.
  • Experience with ML frameworks such as PyTorch and TensorFlow.
  • Experience building and productionizing innovative end-to-end Machine Learning systems.
  • Proficiency in one or more coding languages such as Python, Java, Go, or C++.
  • Experience with any of the following: Spark, Hive, Kafka, Cassandra.

Nice to have

  • Publications at industry recognized ML conferences.
  • Experience in simplifying/converting business problems into ML problems.
  • Experience developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability.

What the JD emphasized

  • recommendation system research experiences
  • strong focus on machine learning and recommendation systems
  • deep learning, recommendation systems, or optimization algorithms
  • building and productionizing innovative end-to-end Machine Learning systems
  • Feed Model ML Quality
  • Model Serving foundation

Other signals

  • productionizing recommendation models
  • end-to-end ML systems
  • Feed Model ML Quality
  • Model Serving foundation