Uber Corporate

Uber Corporate

Senior Machine Learning Engineer, Rider (Multiple Teams)

San Francisco, California, USA · Senior · Full-time

Sponsorship not specified$202k-$224kDetected 66 days ago
PythonJavaGoDistributed SystemsSQLMachine LearningDeep LearningTensorFlowPyTorchSparkData EngineeringData ScienceNLPStatisticsA/B TestingResearchLeadershipCollaborationProblem Solving

About the role

  • We stay at the forefront of innovation by employing cutting-edge techniques like multi-task learning, sequence modeling, and transformers, applying statistical and operations research principles globally.
  • As a software engineer, you will work with Machine Learning Engineers (MLEs) to deploy and enhance these models-such as sequential recommendation systems-to improve the user experience and business metrics.

Responsibilities

  • Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress.

Nice to have

  • About the Teams The Aura team powers a real-time ML engine personalizing the booking experience for millions of riders.
  • Bachelor's degree in Computer Science, Engineering, Mathematics or related field.
  • Strong problem-solving skills, with expertise in ML methodologies.
  • programming languages such as Python, Spark SQL, Presto, Java, Go Preferred Qualifications.
  • 5+ years of experience in software engineering specializing in applied ML methods.
  • PhD degree in Computer Science,

Compensation

  • The base salary range for this role is USD$202,000 per year - USD$224,000 per year.

Benefits

  • You will also be eligible for various benefits.
  • For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp.

Equal opportunity

  • Equal Opportunity employer.
  • If you have a disability or special need that requires accommodation, please let us know by completing [this form](;br> Offices continue to be central to collaboration and Uber's cultural identity.
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This listing is sourced directly from Uber Corporate's careers page and normalized into a canonical job model.