About the Role
As a Sr Machine Learning Engineer II on the Help Intelligence team (Uber Customer Obsession org), you help architect, implement, and maintain machine learning (ML), natural language process (NLP), Conversational AI (CovAI), and Search models that fits seamlessly to the highly-performant, low-latency, reliable, scalable distributed systems used by hundreds of millions of riders and eaters, millions of drivers and delivery partners every day. You will work closely with our internal stakeholders across all Uber business verticals: from Mobility (Driver, Rider), to Delivery, to Freight, and others. On the financial side of things, these lines of business bring in a total revenue of 65 billion dollars a year (2019)!
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You will have an outstanding opportunity to solve engineering problems for one of the largest best-in-class support ecosystems on the planet with state-of-the-art technologies. You will have a chance to help Uber customers discover the support contents and automations, and triage their support needs across all the communication channels: phone, chat, message, and video - in thousands of cities around the globe.
What You Will Do
• Leading the innovative ML/NLP/CovAI/Search solutions development for challenging business problems that are fundamental to promote intelligence, automation, and personalization at the outermost layer of the customer obsession for Uber.
• Aligning product, design, operation, data science, and peer engineering teams to analyze key business problems, drive the customer experience metrics, and achieve cost savings and/or efficiency improvement.
• Developing state-of-the-art technologies to tackle the breadth of the problems involved in this area and performing constant solution adjustments arising from evolving user behaviors (e.g. post-covid).
• Helping build a predictions engine that can support a wide variety of personalization and contextual recommendations use cases.
• Writing quality documentation, delivering enduring value in terms of software and modeling artifacts, mentoring junior colleagues, and fostering a strong culture of quality.
Basic Qualifications
• Deep Learning
• Scalable ML architecture
• Feature management
Preferred Qualifications
• Personalization
• Ability to deliver end-to-end solutions, including data preparation, training, deployment, and monitoring.
• Experience working with product/business partners and ability to work with ambiguous problem definitions.
• Expertise in recommendation systems, or deep learning.
• Experience with any of the following: Google DialogFlow, Kafka, ElasticSearch, Spark, Hive.
• Ability to innovate, as proven by a track record of software artifacts or publications.
• Experiences in mentoring junior colleagues and fostering a collaborative team culture.