Overview
On Site
USD 223,000.00 - 248,000.00 per year
Full Time
Skills
Real-time
Ideation
Operational Efficiency
Art
Forecasting
User Experience
Computer Science
Mathematics
Software Engineering
C
C++
Java
Python
Data Structure
Algorithms
Management
TensorFlow
PyTorch
Caffe
scikit-learn
Apache Spark
SQL
Database
Apache Hive
Apache Kafka
Apache Cassandra
Training
Machine Learning (ML)
IT Management
Mentorship
Deep Learning
Optimization
Online Learning
Law
Legal
Collaboration
Job Details
About the Role
Delivery Marketplace is a central pillar of Uber's delivery products, serving as the \"brain\" of the operation. We drive every decision that enables orders to go from point A to point B - from Uber Eats to newer verticals like Uber Grocery. We're responsible for everything: from dispatch decisions, predicting delivery times, and optimizing pickup times, to ensuring we deliver the most efficient and impactful solutions for Uber's most critical business goals.
As a Staff Machine Learning Engineer (MLE), you will lead cutting-edge efforts within the Delivery Marketplace organization, developing optimization solutions using real-time and machine learning signals to solve complex, strategically important challenges. You will work on critical systems that directly impact Uber's top and bottom lines and have the opportunity to work with a talented team of engineers, product managers, and data scientists to build solutions at scale.
This is an opportunity to tackle some of the most complex, real-time optimization problems at Uber scale. You will lead a team of talented MLEs, work with diverse stakeholders, and directly influence the future of Uber's delivery experience. You will help Uber deliver both efficiency and a magical user experience - impacting millions of people across the world.
What You Will Do
- Lead the design and development of machine learning solutions that optimize real-time operations across Uber's Delivery Marketplace.
- Build advanced ML models using techniques such as reinforcement learning, deep learning, and optimization methods to improve efficiency and the user experience.
- Lead and mentor a team of MLEs, providing technical leadership, setting the vision, and guiding the team through the end-to-end development process - from ideation to model deployment and scaling.
- Collaborate closely with cross-functional teams (product managers, data scientists, engineers) to define high-impact problems and develop solutions that improve operational efficiency while delivering magical user experiences.
- Solve complex, real-world problems using state-of-the-art forecasting, demand-supply models, and prediction models for food prep time, batching quality, and courier activity at restaurants.
- Balance business objectives and user experience by developing objective functions that optimize both business performance and user satisfaction.
Basic Qualifications
- PhD or equivalent in Computer Science, Engineering, Mathematics or related field AND 2-years full-time Software Engineering work experience OR 5-years full-time Software Engineering work experience, WHICH INCLUDES 3-years total technical software engineering experience in one or more of the following areas:
- Programming language (e.g. C, C++, Java, Python, or Go)
- Large-scale 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 with SQL and database systems such as Hive, Kafka, Cassandra, etc
- Experience in the development, training, productionization and monitoring of ML solutions at scale.
Preferred Qualifications
- Experience in a technical leadership role and mentoring junior engineers.
- Experience in modern deep learning architectures and probabilistic models.
- Experience in optimization (RL / Bayes / Bandits) and online learning.
- Experience in causal inference/personalization/ranking
For San Francisco, CA-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year.
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. 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.
Delivery Marketplace is a central pillar of Uber's delivery products, serving as the \"brain\" of the operation. We drive every decision that enables orders to go from point A to point B - from Uber Eats to newer verticals like Uber Grocery. We're responsible for everything: from dispatch decisions, predicting delivery times, and optimizing pickup times, to ensuring we deliver the most efficient and impactful solutions for Uber's most critical business goals.
As a Staff Machine Learning Engineer (MLE), you will lead cutting-edge efforts within the Delivery Marketplace organization, developing optimization solutions using real-time and machine learning signals to solve complex, strategically important challenges. You will work on critical systems that directly impact Uber's top and bottom lines and have the opportunity to work with a talented team of engineers, product managers, and data scientists to build solutions at scale.
This is an opportunity to tackle some of the most complex, real-time optimization problems at Uber scale. You will lead a team of talented MLEs, work with diverse stakeholders, and directly influence the future of Uber's delivery experience. You will help Uber deliver both efficiency and a magical user experience - impacting millions of people across the world.
What You Will Do
- Lead the design and development of machine learning solutions that optimize real-time operations across Uber's Delivery Marketplace.
- Build advanced ML models using techniques such as reinforcement learning, deep learning, and optimization methods to improve efficiency and the user experience.
- Lead and mentor a team of MLEs, providing technical leadership, setting the vision, and guiding the team through the end-to-end development process - from ideation to model deployment and scaling.
- Collaborate closely with cross-functional teams (product managers, data scientists, engineers) to define high-impact problems and develop solutions that improve operational efficiency while delivering magical user experiences.
- Solve complex, real-world problems using state-of-the-art forecasting, demand-supply models, and prediction models for food prep time, batching quality, and courier activity at restaurants.
- Balance business objectives and user experience by developing objective functions that optimize both business performance and user satisfaction.
Basic Qualifications
- PhD or equivalent in Computer Science, Engineering, Mathematics or related field AND 2-years full-time Software Engineering work experience OR 5-years full-time Software Engineering work experience, WHICH INCLUDES 3-years total technical software engineering experience in one or more of the following areas:
- Programming language (e.g. C, C++, Java, Python, or Go)
- Large-scale 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 with SQL and database systems such as Hive, Kafka, Cassandra, etc
- Experience in the development, training, productionization and monitoring of ML solutions at scale.
Preferred Qualifications
- Experience in a technical leadership role and mentoring junior engineers.
- Experience in modern deep learning architectures and probabilistic models.
- Experience in optimization (RL / Bayes / Bandits) and online learning.
- Experience in causal inference/personalization/ranking
For San Francisco, CA-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year.
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. 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.