Software Engineer II

Uber Uber · Consumer · Sunnyvale, CA · Engineering

Software Engineer II role at Uber focused on designing, building, and operating end-to-end features for the AdTech tech stack. This involves creating scalable data pipelines using big data technologies, optimizing ad spend through experimentation, and productionizing ML models. The role requires collaboration with cross-functional teams and leveraging internal Uber technologies like Michelangelo (ML) and Piper (Data orchestrator).

What you'd actually do

  1. Design, build and deliver end-to-end features spanning across the AdTech tech stack.
  2. Design, develop and operate highly scalable and reliable services.
  3. Leverage your experience with large scale data using big data technologies such as Spark, Hive, Presto, Flink, to build scalable data pipelines that power various ad tech use cases.
  4. Work in a cross-functional team along with Product Management, Data Science, and Marketing to experiment with new strategies and approaches to optimize ad spend and then build scalable production systems to productionize the winning strategies.
  5. Collaborate across other engineering teams at Uber to leverage other Uber Internal tech stacks and systems like Michelangelo (ML), Bullseye (Audience Builder), Morpheus (Experimentation), Flow (Workflow Automation), Piper (Data orchestrator) etc. to build systems that leverage mature technologies at Uber and also influence the overall roadmap of these technologies.

Skills

Required

  • C++
  • Python
  • Java
  • GIT
  • SQL
  • Data structures and algorithms
  • Designing technology stacks
  • Debugging and monitoring for production services
  • Distributed systems
  • Software Development Lifecycle
  • Real Time Data Systems
  • Streaming and batch Data Driven Production Systems
  • Service Oriented Architecture

Nice to have

  • Spark
  • Hive
  • Presto
  • Flink
  • Michelangelo
  • Bullseye
  • Morpheus
  • Flow
  • Piper

What the JD emphasized

  • Productionizing Machine Learning Models

Other signals

  • productionizing ML models
  • ad tech use cases
  • optimize ad spend
  • scalable production systems