Uber AI's mission is to optimize and innovate Uber's products and business using machine learning and AI. The group consists of Uber's machi...
Uber AI's mission is to optimize and innovate Uber's products and business using machine learning and AI. The group consists of Uber's machine learning platform team which enables machine learning at scale, AI building blocks which enable product teams to build unique experiences and engagements with product teams on their business problems.
The group consists of machine learning engineers, mobile engineers, backend engineers and research scientists and engineers.
About the Role
Uber AI Building Blocks establishes core, reusable ML systems that can be applied across problems. Through close collaboration we deliver innovative Machine Learning/AI solutions for core business problems.
We focus on productionizing ML to improve end user experiences and drive the business. As a core offering, we are developing a platform around recommendation and personalization across Uber products. We strive to understand our riders, drivers, restaurants and build more personal experience across all users. As an engineer, you would be responsible for building these systems that are high performant and can operate at high scale (imagine every Uber trip) while leveraging state-or-art research in Recommendations. You will deliver these solutions from inception to production.
What You'll Do
- Develop innovative ML/AI solutions for challenging business problems that are fundamental for Uber
- Partner with product teams to analyze key business problems
- Collaborate with data science and engineering teams to integrate and validate machine learning solutions end-to-end
- Deliver enduring value in terms of software and modeling artifacts
- Help build a predictions engine that can support a wide variety of personalization and recommendations use cases
- Use Knowledge base and Knowledge graphs to build product recommendations across Uber's suite of products
- 2+ years of industry experience in applied ML, or a Ph.D. with some industry experience obtained through e.g. internships
- Proficiency in Python, Java
- Experience running production engineering systems
- Experience with ML frameworks such as PyTorch and TensorFlow
- 4+ years of industry experience in applied ML, or a Ph.D. and 2+ years of industry experience
- Expertise in Bayesian optimization, probabilistic machine learning, knowledge graphs, recommendation systems, or deep learning
- Proficiency in one or more coding languages such as Java, Go, C, C++
- Experience with any of the following: Spark, Hive, Kafka, Cassandra
- Ability to innovate, as proven by a track record of software artifacts or publications
- Ability to deliver end-to-end solutions, including data preparation, training, and deployment
- Experience working with product teams
- Ability to work with ambiguous problem definitions
- Proven ability to communicate technical knowledge to a business audience
- Collaborative attitude and constructive approach
- provided by Dice