About the Role
The Shopping Ranking Team mission is enabling eaters to effortlessly make shopping decisions and find what they need. We pursue this mission via an ML-driven algorithmic approach, applying state-of-the-art Machine Learning (ML), Optimization techniques to learn from massive datasets Uber has, and build a scalable and reliable shopping intelligence ranking and recommendation systems.
We are actively seeking individuals who excel in problem-solving and critical thinking, are proficient in coding, with proven track records of learning and growth, and have a deep interest in ML model, feature and infrastructure development. Candidates will have the opportunity to work across various lines, from infrastructure development to ML model development, productionalization, offering a diverse and enriching experience. Join us in our pursuit of excellence as we are building the next generation of shopping ranking and recommendation systems.
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---- What the Candidate Will Do ----
- Design and build Machine Learning models in Ranking and Recommendation domain.
- Productionize and deploy these models for real-world application.
- Review code and designs of teammates, providing constructive feedback.
- Collaborate with Product and cross-functional teams to brainstorm new solutions and iterate on the product.
---- Basic Qualifications ----
- Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 2+ years of full-time engineering experience.
- 1+ years of ML experience and building ML models
- Experience working with multiple multi-functional teams(product, science, product ops etc).
- Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C++).
- Experience with big-data architecture, ETL frameworks and platforms, such as HDFS, Hive, MapReduce, Spark, , etc.
- Working knowledge of latest ML technologies, and libraries, such as PyTorch, TensorFlow, Ray, etc.
- Proven track records of being a fast learner and go-getter, with willingness to get out of the comfort zone.
---- Preferred Qualifications ----
- Experience with building ranking and recommendation systems in production, making practical tradeoffs among algorithm sophistication, compute complexity, maintainability, and extensibility in production environments.
- 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 design and architecture of ML systems and workflows.
- Experience owning and delivering a technically challenging, multi-quarter project end to end.
For San Francisco, CA-based roles: The base salary range for this role is USD$158,000 per year - USD$175,500 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$158,000 per year - USD$175,500 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.
Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.
Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.