Staff Machine Learning Engineer

at  Uber

- Basic Qualifications -

• PhD or Master in relevant fields (CS, EE, Math, Stats, etc.) with recommendation system research experiences and 8 years minimum of industry experience with a strong focus on machine learning and recommendation systems.
• Expertise in deep learning, recommendation systems, or optimization algorithms.
• Experience with ML frameworks such as PyTorch and TensorFlow.
• Experience building and productionizing innovative end-to-end Machine Learning systems.
• Proficiency in one or more coding languages such as Python, Java, Go, or C++.
• Experience with any of the following: Spark, Hive, Kafka, Cassandra.
• Strong communication skills and can work effectively with cross-functional partners

- Preferred Qualifications -

• Publications at industry recognized ML conferences.
• Experience in simplifying/converting business problems into ML problems.
• Experience designing and developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability.Experiences leading projects with org-level impact

The UberEats Feed is the front door to our service. It serves an important role for both users and merchants. For our users, the Feed helps them find a great restaurant or grocery store for their needs. It also serves as an important gateway for them to explore the breadth and depth of UberEats’s selection. For merchants, it is the main surface for which they get in front of potential customers to showcase their products. As a Machine Learning Engineer in this role, you will be able to work on various open-ended, challenging, impactful problems.- What the Candidate Will Do -

• Innovate and productionize start-of-the-art recommendation models, and customize for Uber’s use cases.
• Design and build the end-to-end large-scale ML systems to power the HomeFeed Recommendation.
• Improve the Feed Model ML Quality, Model Serving foundation and the Data foundation.
• Collaborate with cross-functional and cross-team stakeholders.
• Lead a team of strong machine learning engineers and scientists to deliver high-impact projects.
• Drive the long-term technical vision for the team.

• Drive cross-team initiatives with org level impact.

• Basic Qualifications -

• PhD or Master in relevant fields (CS, EE, Math, Stats, etc.) with recommendation system research experiences and 8 years minimum of industry experience with a strong focus on machine learning and recommendation systems.

• Expertise in deep learning, recommendation systems, or optimization algorithms.
• Experience with ML frameworks such as PyTorch and TensorFlow.
• Experience building and productionizing innovative end-to-end Machine Learning systems.
• Proficiency in one or more coding languages such as Python, Java, Go, or C++.
• Experience with any of the following: Spark, Hive, Kafka, Cassandra.

• Strong communication skills and can work effectively with cross-functional partners.

• Preferred Qualifications -

• Publications at industry recognized ML conferences.

• Experience in simplifying/converting business problems into ML problems.

• Experience designing and developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability.Experiences leading projects with org-level impact.

• For New York, NY-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 per year. For Seattle, WA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 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 https://www.uber.com/careers/benefits