Uber Corporate
Machine Learning Engineer II
San Francisco, California, USA · Mid · Full-time
Sponsorship not specified$171k-$190kDetected 97 days ago
PythonJavaScalaAlgorithmsCode ReviewMachine LearningDeep LearningTensorFlowPyTorchscikit-learnSparkData EngineeringMLOpsStatisticsA/B TestingResearchCommunicationCollaboration
About the role
- Uber's Marketplace is at the core of the business.
- For San Francisco, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year.
Responsibilities
- In this role, you will collaborate with senior engineers, product managers, and data scientists to implement technical solutions, navigate trade-offs, and maintain reliable production systems.
- Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress.
Nice to have
- About the Role Uber's Marketplace is at the core of the business.
- Your work will directly impact marketplace efficiency and empower earning opportunities for millions of drivers worldwide.
- Experience operating and monitoring ML models in a production setting, with a basic understanding of MLOps workflows..
- Ability to translate well-defined business problems into actionable ML tasks, prototype ideas quickly, and move projects from conception to production..
Compensation
- The base salary range for this role is USD$171,000 per year - USD$190,000 per year.
Benefits
- You will also be eligible for various benefits.
- We are seeking a Machine Learning Engineer to help build and scale the technical foundations behind Uber's driver incentive systems.
- 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
- Uber is proud to be an Equal Opportunity employer.
- 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.