About The Role
We are looking for a highly motivated GenAI Engineer to join the Customer Obsession team. You will play a critical role in designing conversational GenAI systems and algorithms which would enhance the customer support experience and resolution speed for millions of Uber Eats users worldwide while making O(100s millions) cost savings. You will leverage your expertise in data analysis, machine learning, and engineering to drive insights, identify tech-driven product innovations, optimize algorithms and systems ultimately improving user satisfaction and operational efficiency.
What The Candidate Will Do
- Design, develop, and productionize machine learning (ML) solutions in the field of customer support engineering spanning generative AI algorithms, agentic AI design at scale, NLP for query understanding and ranking responses, distillation techniques, etc.
- Productionize and deploy these models for real-world applications in customer support.
- Design and analyze experiments using a combination of data analysis/statistical analysis to lead the team to a reasonable inference.
- Review code and designs of teammates, providing constructive feedback.
- Collaborate with cross-functional teams to brainstorm new solutions and iterate on the product.
- Mentor junior engineers.
What The Candidate Will Need
- Bachelor's or Master's in Computer Science, Statistics, or a related field or Equivalent Experience
- Minimum 5 years of experience in industry with a strong focus on machine learning and optimization.
- Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
- Solid understanding of statistical analysis and feature engineering techniques.
- Excellent communication and collaboration skills.
- Ability to work independently and take ownership of projects.
- Experience using SQL in a production environment.
- Experience in experimental design and analysis, exploratory data analysis, and statistical analysis.
- Experience with dashboarding and using data visualization tools.
- Experience using statistical methodologies such as sampling, statistical estimates, descriptive statistics, or similar.
