About the Role
- - - - 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.
- - - - Basic Qualifications ----
- PhD or Master in relevant fields (CS, EE, Math, Stats, etc.) with recommendation system research experiences and 4 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 developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability.
For New York, NY-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year.
For San Francisco, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year.
For Seattle, WA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year.