Job Description

About the role and team

Leading programs at Uber means setting direction in motion, not waiting for perfect conditions. As a Program Manager, you’ll be responsible for the strategy and execution of initiatives that directly impact how riders, earners, and merchants move through the Uber ecosystem. You'll navigate ambiguity and tackle complex operational challenges to drive real-world impact.

This role requires a mindset that thrives in challenging situations, balancing technical rigor with a deep obsession for the customer journey. You’ll need to influence stakeholders and align diverse teams without relying on control, turning complexity into clarity at a global scale. If you are motivated by the chance to shape the future of real-world movement through structured, data-backed execution, this is where you’ll make your mark.

Rate of Pay: $131,000 to $160,000 per Year

You will be eligible to participate in Uber's bonus program, and may be offered 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.

What you’ll do

• Define and implement agent-customer communication standards.
• Help define quality for user-facing support bots.
• Conversational Designer for Uber Customer Support.
• Work with Product and Engineering to update the Response Generation prompt.
• Evaluate the performance of our agent interactions at scale.
• Optimize support resolution steps for interaction efficiency.
• Work with engineering teams to optimize how knowledge is used within a customer-support conversation.
• Design prompt improvements for chatbots powered by LLMs.
• Develop, own, and implement short & long-term strategies for agent responses in support interactions.
• Work with our Metadata team to improve intent identification.
• Continuously enhance all agent-response products to ensure improvement to key metrics & user efficacy.
• Coordinate strategic projects between GDX, CO, and Global Content.
• May telecommute.
  

Basic Qualifications

• Employer will accept a Master's degree in Conversational Design, Business, Project Management, Information and Knowledge Strategy, or related field and 3 years of experience in the job offered or in a related occupation.
• Position requires:
  • Prompt engineering and optimization for LLMs to optimize responses, design structured task flows, and improve model accuracy and efficiency in dynamic contexts;
  • Applying large language models (LLMs) to extract insights, automate processes, and generate high-quality natural language outputs for business or technical use cases;
  • Stakeholder management skills, including in delivering change management projects to launch new enterprise-wide tools or systems;
  • Building retrieval-augmented generation (RAG) chatbots with a vector database, similarity search-based reranking and retrieval, and response generation for combining proprietary data sources with generative AI models that ensures factual and contextually grounded outputs;
  • Model fine-tuning for customizing pre-trained LLMs on domain-specific datasets, improving performance on targeted tasks such as Q&A through at least one of the following: HuggingFace’s API, OpenAI’s API;
  • Implementing Python code for data analysis, automation, API integration, and machine learning pipeline development, including libraries like Pandas, NLTK, Scikit-learn, or PyTorch, and utilizing cloud environments like Google Cloud Platform (GCP) for deploying and managing machine learning models, orchestrating data workflows, and scaling compute resources efficiently;
  • Qualitative analysis of text data, including thematic analysis and sentiment analysis to interpret customer feedback, identify emerging issues, and inform product, marketing, or support strategies;
  • Designing a knowledge management strategy, roadmap, and implementation framework to ensure that stakeholders can share and access the needed information and knowledge at the right time;
  • Designing a continuous improvement process through retrospectives, lessons learned capturing, and cycles of feedback integration;
  • Information Architecture for maintaining metadata, tagging, findability, and knowledge retrieval systems;
    Information organization for organizing, structuring, and maintaining high-quality documentation and knowledge assets;
  • Understanding of knowledge base content curation and best practices, including background in content creation and curation, copy-editing and style guidelines that inform conversational design.

Ready to Ride?