Sr Software Engineer - Machine Learning, Marketplace/maps/membership/av

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

This role focuses on designing, developing, optimizing, and productionizing machine learning (ML) or ML-based solutions and systems at scale for Uber's various platforms (Marketplace, Maps, Membership, AV). The engineer will also contribute to ML infrastructure for model development, training, deployment, and scaling. Key responsibilities include collaborating with stakeholders, writing efficient code for low-latency and high-reliability models, and implementing monitoring systems for live environments. The role emphasizes the full ML lifecycle, from development to productionization and monitoring, with a focus on scalable and reliable ML systems.

What you'd actually do

  1. Design, build, and deploy scalable machine learning models to production to solve real-world business problems.
  2. Collaborate with cross-engineering teams, data scientists and other partners to gather requirements and translate them into technical specification
  3. Work closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction.
  4. Write clean, testable, and efficient code to ensure models run with low latency and high reliability.
  5. Implement monitoring systems to track model performance, stability, and data drift in live environments.

Skills

Required

  • Machine Learning
  • AI
  • Data Science
  • Computer Science
  • Engineering
  • Mathematics
  • Java
  • C++
  • Python
  • Go
  • ML algorithms/modeling
  • training
  • productionization
  • monitoring of ML solutions at scale

Nice to have

  • full ML lifecycle
  • model deployment
  • containerization
  • workflow orchestration
  • optimization techniques
  • algorithmic development
  • algorithms
  • data structures
  • complexity analysis
  • code reviews
  • documentation
  • unit and integration testing

What the JD emphasized

  • productionize machine learning (ML) or ML-based solutions and systems
  • productionize machine learning models
  • models run with low latency and high reliability
  • track model performance, stability, and data drift in live environments

Other signals

  • productionize machine learning (ML) or ML-based solutions and systems
  • productionize machine learning models
  • models run with low latency and high reliability
  • track model performance, stability, and data drift in live environments