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Software Engineer : Build Scalable Systems & ML Apps

Job in Sunnyvale, Santa Clara County, California, 94087, USA
Listing for: Uber
Full Time position
Listed on 2026-07-03
Job specializations:
  • Software Development
    Software Engineer, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 187741 - 225289 USD Yearly USD 187741.00 225289.00 YEAR
Job Description & How to Apply Below
Position: Software Engineer I: Build Scalable Systems & ML Apps

Employer: Uber Technologies, Inc.

Job Title: Software Engineer

Job Location: Sunnyvale, California

Job Type: Full Time

Rate of Pay: $187,741 to $225,289 per year

You will be eligible to participate in Uber's bonus program, and may be offered other types of comp. You will also be eligible for various benefits. More details can be found at the following link  .

Duties: Design, develop, and test software applications. Design, develop, and/or test software applications using company technology stack. Apply technical knowledge to evaluate complex data, developments, and projects in order to provide recommendations to resolve technical issues. Support the company's business operations and improve engineering productivity. Build software components that can be integrated into a fully functional software system. Solve production issues in product and system reliability.

May telecommute.

Employer will accept a Master's degree in Computer Science, Engineering (Any), Intelligent Information systems, Information Technology, Mathematics, Physics, or related field.

Position Requires

  • C++, Python, Go, Java, GIT, or SVN;
  • SQL or MySQL;
  • Data structures and algorithms development and functionality;
  • Designing technology stacks;
  • Debugging and monitoring for production services;
  • Distributed systems;
  • Software Development Lifecycle;
  • Machine learning and deep learning;
  • Common ML frameworks including Tensor Flow or PyTorch;
  • Large-scale data processing tools including Spark;
  • Optimization techniques;
  • Experimentation platforms and statistical methods for evaluating model performance.
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