**About the Role** Uber is a fairly operational business. Considering that we operate a large number of countries and individual cities that are governed by their own regulations and compliance requirements as well as their own nuances, our core products have to be configured as well as localized to reflect the regional nuances. This can be from configuring and diagnosing surge pricing, to configuring safety controls, annotating pick ups and drop offs on maps as well as reflecting the right attributes to enable better routing of trips. To enable such workflows, we need human intelligence - from curating data, to labeling artifacts, to reviewing the work of the algorithms so we can improve them. The responsibility of the this team is not just to provide those tools that enable ease of operations, but also over time to **reduce the need for the human in the loop and bake in automation to continually improve the quality of our trips**. This team is responsible for platforms that power easy task allocation and management as well as analytics. There are a lot of opportunities in this space to identify the right set of problems that can be solved using ML, **some examples being auto transcription of documents, masking of PII data to address privacy concerns, etc.** In addition, the team is responsible for the labeling platform that powers ML annotations and label stores so that it is seamlessly integrated into active learning frameworks. Creating annotations is a highly manual process today. However there is a large opportunity to **integrate advanced ML models to enable ML assisted annotations for use cases like GenAI Labeling, Image / audio / video Classification, Image / Video segmentation, etc. We are also expanding into Lidar annotation space, which opens up a unique set of problems to solve in the 3D labeling world.** **\-\-\-\- What the Candidate Will Do ----** - Identify and pitch the right problems that will benefit from ML expertise. - Lead the design and development of a suite of ML models that will help solve the above problems. - Collaborate with backend and frontend engineers to integrate your solutions in our products & platforms. - Work with cross functional counterparts like ML Ops team to understand their needs and improve the models accordingly. - Write clean, modular, and maintainable code. - Conduct code reviews and ensure high code quality standards for your team. - Keep up to date with the latest ML technologies and best practices. **\-\-\-\- Basic Qualifications ----** - Engineering Degree or equivalent in Computer Science, related field - Experience in one or more Programming languages (e.g. C, C++, Java, Python, or Go) - Training using data structures and algorithms - Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning) - Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib - Good problem-solving and analytical skills - Good team player, collaboration and leadership skills. **\-\-\-\- Preferred Qualifications ----** - 10+ years of professional software engineering experience in the industry - Experience deconstructing real problems, including selecting the proper data structure and developing an appropriate algorithm and pushing to production - Experience designing ML solutions, and implementing those solutions at scale - Scalable ML architecture - Deep Learning experience - Experience collaborating with backend & frontend engineers to take your solutions to production We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let’s move the world forward, together. 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. \*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to [accommodations@uber.com](mailto:accommodations@uber.com).