What the Candidate Will Do:
- Innovate and productionize start-of-the-art recommendation models, and customize them for Uber’s use cases.
- Design and build the end-to-end large-scale ML systems to power Home Feed recommendation systems.
- Lead research and design of new recommendation algorithms and systems.
- Collaborate with cross-functional and cross-team stakeholders to solve open-ended user and technical problems.
- Lead a team of strong machine learning engineers and xfn to deliver high-impact projects.
- Drive the long-term technical vision for the team in a specific recommendations domain.
- 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++.
Preferred Qualifications:
- Publications at industry recognized ML conferences.
- Experience with any of the following: Spark, Hive, Kafka, Cassandra.
- Strong communication skills and can work effectively with cross-functional partners.
- 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 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.