Overview
On Site
USD 198,000.00 - 220,000.00 per year
Full Time
Skills
Fusion
Predictive Modelling
Data Analysis
Workflow
Knowledge Base
Innovation
Roadmaps
Evaluation
Data Quality
Computer Science
Mathematics
Software Engineering
C
C++
Java
Python
Training
Data Structure
Management
TensorFlow
PyTorch
Caffe
scikit-learn
Apache Spark
Lifecycle Management
Statistical Models
Communication
Geospatial Analysis
Location-based Services
Sensors
Data Processing
Cloud Computing
Amazon Web Services
Google Cloud Platform
Google Cloud
Machine Learning (ML)
Algorithms
Real-time
Time Series
Law
Legal
Collaboration
Job Details
About The Role
As a Machine Learning Engineer on the Location & Sensors team at Uber, you will have the unique opportunity to work on cutting-edge technologies that directly enhance Uber's geospatial capabilities. Our team is at the heart of improving location accuracy and sensor data processing, powering core Uber products and services, from ride-hailing to food and grocery delivery. You will tackle complex challenges in sensor fusion, predictive modeling, and location data analysis, driving innovations that impact millions of users globally.
This role offers the chance to work on systems that are crucial to the efficiency and reliability of Uber's operations, enabling seamless and accurate movement across the world. You will collaborate with a talented, interdisciplinary team to build impactful solutions that scale across Uber's vast ecosystem.
What You Will Do
- Develop and deploy advanced machine learning models to analyze and improve location and sensor data, ensuring accurate positioning and robust data insights.
- Collaborate cross-functionally with engineers, data scientists, and product teams to integrate machine learning solutions into Uber's core services.
- Design and build scalable data pipelines to support end-to-end machine learning workflows and ensure reliable data processing at Uber's global scale.
- Optimize model performance, continually evaluating and refining models to meet the evolving needs of Uber's products.
- Document methodologies and results, contributing to the team's knowledge base and sharing insights to promote innovation across the company.
- Contribute to the technical roadmap by proposing new ideas, setting best practices, and guiding the team towards long-term success.
- Work closely with operations teams to curate high-quality datasets for model evaluation and training, ensuring data quality and consistency.
Basic Qualifications
- PhD or equivalent in Computer Science, Engineering, Mathematics or related field OR 4-years full-time Software Engineering work experience, WHICH INCLUDES 2-years total technical software engineering experience in one or more of the following areas:
- Programming language (e.g. C, C++, Java, Python, or Go)
- Training using data structures and algorithms
- Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning)
- Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib
- Experience in developing and deploying machine learning models
- Experience developing model lifecycle management systems
- Understanding of statistical modeling and machine learning algorithms
- Excellent written and verbal communication skills, including the ability to document models and results
Preferred Qualifications
- Experience with geospatial data and location-based services
- Familiarity with sensor data processing and analysis
- Experience deploying machine learning models in production environments
- Experience with cloud platforms (e.g., AWS, Google Cloud Platform).
- Demonstrated ability to ship high-quality models and features on schedule
- Experience implementing complex projects with multiple dependencies
- Experience with distributed systems
- Familiarity with ML algorithms for detection and classification of data anomalies, such as real-time anomaly detection, Outlier detection, and time series analysis
For San Francisco, CA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.
You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link [](;br>
Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. 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. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.
As a Machine Learning Engineer on the Location & Sensors team at Uber, you will have the unique opportunity to work on cutting-edge technologies that directly enhance Uber's geospatial capabilities. Our team is at the heart of improving location accuracy and sensor data processing, powering core Uber products and services, from ride-hailing to food and grocery delivery. You will tackle complex challenges in sensor fusion, predictive modeling, and location data analysis, driving innovations that impact millions of users globally.
This role offers the chance to work on systems that are crucial to the efficiency and reliability of Uber's operations, enabling seamless and accurate movement across the world. You will collaborate with a talented, interdisciplinary team to build impactful solutions that scale across Uber's vast ecosystem.
What You Will Do
- Develop and deploy advanced machine learning models to analyze and improve location and sensor data, ensuring accurate positioning and robust data insights.
- Collaborate cross-functionally with engineers, data scientists, and product teams to integrate machine learning solutions into Uber's core services.
- Design and build scalable data pipelines to support end-to-end machine learning workflows and ensure reliable data processing at Uber's global scale.
- Optimize model performance, continually evaluating and refining models to meet the evolving needs of Uber's products.
- Document methodologies and results, contributing to the team's knowledge base and sharing insights to promote innovation across the company.
- Contribute to the technical roadmap by proposing new ideas, setting best practices, and guiding the team towards long-term success.
- Work closely with operations teams to curate high-quality datasets for model evaluation and training, ensuring data quality and consistency.
Basic Qualifications
- PhD or equivalent in Computer Science, Engineering, Mathematics or related field OR 4-years full-time Software Engineering work experience, WHICH INCLUDES 2-years total technical software engineering experience in one or more of the following areas:
- Programming language (e.g. C, C++, Java, Python, or Go)
- Training using data structures and algorithms
- Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning)
- Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib
- Experience in developing and deploying machine learning models
- Experience developing model lifecycle management systems
- Understanding of statistical modeling and machine learning algorithms
- Excellent written and verbal communication skills, including the ability to document models and results
Preferred Qualifications
- Experience with geospatial data and location-based services
- Familiarity with sensor data processing and analysis
- Experience deploying machine learning models in production environments
- Experience with cloud platforms (e.g., AWS, Google Cloud Platform).
- Demonstrated ability to ship high-quality models and features on schedule
- Experience implementing complex projects with multiple dependencies
- Experience with distributed systems
- Familiarity with ML algorithms for detection and classification of data anomalies, such as real-time anomaly detection, Outlier detection, and time series analysis
For San Francisco, CA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.
You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link [](;br>
Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. 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. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.