Uber is looking to solve complex, strategically important challenges in membership engagement by developing optimization solutions using real-time and machine learning signals to enhance the user experience and redefine the global membership landscape.
Requirements
- Experience with big-data architecture, ETL frameworks, SQL and database systems such as Hive, Kafka, Cassandra, etc
- 1+ years of experience in the development, training, productionization and monitoring of ML optimization solutions at scale.
- Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C++)
- Experience with taking on vague business problems, translating them into ML + Optimization formulation, identifying the right features, model structure and optimization constraints, and delivering business impact.
- Experience with the design and architecture of ML systems and workflows.
- Experience in modern deep learning architectures, probabilistic models and causal inference/personalization/ranking.
- Experience in optimization (RL / Bayes / Bandits) and online learning.
Responsibilities
- Design and build Machine Learning models responsible for large-scale applied machine learning in optimization and personalization.
- Build high throughput systems that process millions of datapoints each minute and serve hundreds of thousands of QPS
- Collaborate with Product, Science and cross-functional teams to brainstorm new opportunities and solutions for model and product iteration.
- Write high-quality code and uphold standards for testing and coverage.
- Align with the team on solutions to ambiguous problems and analyze the tradeoffs of different technical solutions
- Contribute to engineering cultivation in terms of quality, monitoring, and on-call practices.
Other
- Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 2+ years of full-time engineering experience.
- Proven track records of being a fast learner and go-getter, with willingness to get out of the comfort zone.
- Experience working with multiple multi-functional teams (product, science, product ops etc).
- Willingness to participate in on-call practices.
- Ability to collaborate closely with diverse stakeholders like data scientists, product managers and business in a results-oriented environment.
