About the Role
As a Machine Learning Engineer on the Help Intelligence team (Uber Customer Obsession org), you will help architect, impleme...
About the Role
As a Machine Learning Engineer on the Help Intelligence team (Uber Customer Obsession org), you will help architect, implement, and maintain machine learning (ML), natural language processing (NLP), Conversational AI (CovAI), and Search models that fit seamlessly in the highly-performant, low-latency, reliable, scalable distributed systems used by hundreds of millions of riders and eaters, as well as 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)!
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.
Your day-to-day will involve the following:
- Developing innovative ML/NLP/CovAI/Search solutions for challenging business problems that are fundamental to promote intelligence, automation, and personalization at the outermost layer of the customer obsession for Uber.
- Working closely with partner teams to productize, integrate, and validate ML/NLP/CovAI/Search systems end-to-end to discover and automate Help/Support interactions across all Uber mobile/web applications that have a direct impact on our business growth and customer experience.
- Collaborating with 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.
- Applying 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, and fostering a strong culture of quality.
- Experience with ML frameworks such as PyTorch, TensorFlow, or others
- Experience running production engineering systems.
- At least two (2) years of industry experience in applied ML, or a Ph.D. with some industry experience obtained through e.g. internships.
- Ability to deliver end-to-end solutions, including data preparation, training, and deployment.
- Experience working with product teams and ability to work with ambiguous problem definitions.
- Proven ability to communicate technical knowledge to a business audience.
- Expertise in recommendation systems, or deep learning.
- Experience with any of the following: Google DialogFlow, Kafka, ElasticSearch, Spark, Hive.
- Collaborative attitude and constructive approach.
- provided by Dice