Senior Machine Learning Engineer - Location & Sensors
Apply NowCompany: Uber
Location: San Francisco, CA 94112
Description:
About The Role
As a Machine Learning Engineer on the Location & Sensors team, you will play a crucial role in developing and implementing machine learning models to enhance Uber's geospatial capabilities. The Location & Sensors team is focused on improving location accuracy and sensor data processing to power various Uber products and services. You will work on projects related to location data analysis, sensor fusion, and predictive modeling. This work directly impacts the efficiency and reliability of Uber's core operations, including moving people, food, and groceries from point A to point B.
What You Will Do
- Develop and implement machine learning models for location and sensor data analysis
- Collaborate with the team to improve location accuracy and sensor data processing
- Design and build data pipelines to support machine learning workflows
- Evaluate and optimize model performance
- Document models, methodologies, and results.
- Contribute to the team's technical roadmap and best practices
- Collaborate with ops teams to produce curated datasets for model evaluation and training
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, GCP).
- 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 [https://www.uber.com/careers/benefits](https://www.uber.com/careers/benefits).
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](https://forms.gle/aDWTk9k6xtMU25Y5A).
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, you will play a crucial role in developing and implementing machine learning models to enhance Uber's geospatial capabilities. The Location & Sensors team is focused on improving location accuracy and sensor data processing to power various Uber products and services. You will work on projects related to location data analysis, sensor fusion, and predictive modeling. This work directly impacts the efficiency and reliability of Uber's core operations, including moving people, food, and groceries from point A to point B.
What You Will Do
- Develop and implement machine learning models for location and sensor data analysis
- Collaborate with the team to improve location accuracy and sensor data processing
- Design and build data pipelines to support machine learning workflows
- Evaluate and optimize model performance
- Document models, methodologies, and results.
- Contribute to the team's technical roadmap and best practices
- Collaborate with ops teams to produce curated datasets for model evaluation and training
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, GCP).
- 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 [https://www.uber.com/careers/benefits](https://www.uber.com/careers/benefits).
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](https://forms.gle/aDWTk9k6xtMU25Y5A).
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.