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

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

Software Engineer II focused on designing, developing, optimizing, and productionizing machine learning models and systems at scale for Uber's marketplace, maps, and membership platforms. Responsibilities include writing efficient code for low-latency, high-reliability models, implementing monitoring systems, and collaborating with cross-functional teams. Requires experience with the full ML lifecycle, including deployment and orchestration.

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. Write clean, testable, and efficient code to ensure models run with low latency and high reliability.
  4. Implement monitoring systems to track model performance, stability, and data drift in live environments.
  5. Stay up-to-date with standard machine learning algorithms and industry trends to continuously improve our tech stack.

Skills

Required

  • Bachelor’s degree or equivalent in Machine Learning, AI, Data Science, Computer Science, Engineering, Mathematics or related field with at least 1 year of full-time Machine Learning work experience OR PhD in Machine Learning, AI, Data Science, Computer Science, Engineering, Mathematics or related field
  • Proficiency in at least one programming language such as Java, C++, Python, or Go
  • 1 year of experience with ML algorithms/modeling- developing, training, productionization and monitoring of ML solutions at scale.

Nice to have

  • Master’s degree or higher in Machine Learning, AI, Data Science, Computer Science, Engineering, Mathematics or related field.
  • More than 3 years of full-time machine learning work experience
  • Experience with the full ML lifecycle (at Uber Scale), including model deployment, containerization and workflow orchestration.
  • Experience in translating ambiguous business problems into technical solutions in a structured and principled way.
  • Strong communication skills, including through documentation and design discussions
  • Experience with optimization techniques and algorithmic development
  • Strong problem-solving skills, with expertise in algorithms, data structures, and complexity analysis
  • High bar for quality as demonstrated by code reviews, documentation, unit and integration testing

What the JD emphasized

  • productionize machine learning (ML) or ML-based solutions and systems
  • models run with low latency and high reliability
  • full ML lifecycle (at Uber Scale), including model deployment, containerization and workflow orchestration

Other signals

  • productionize machine learning (ML) or ML-based solutions and systems
  • design, build, and deploy scalable machine learning models to production
  • models run with low latency and high reliability
  • monitoring systems to track model performance, stability, and data drift in live environments
  • full ML lifecycle (at Uber Scale), including model deployment, containerization and workflow orchestration