Job Description

About the Role



We are seeking a talented and entrepreneurial Sr Software Engineer for Uber's Delivery Search Data Foundations and Measurement team. The team seeks to supercharge the quantity, quality, freshness, and reliability of the data and measurements backing the Uber Eats search experience, across both our established online food delivery business and our early-lifecycle grocery & retail vertical.

What the Candidate Will Do:

Design and implement scalable pipelines for ML data preparation, training, and prediction; and/or for LLM-driven measurement of Search result quality.

Collaborate with Science and Product partners to define and prioritize next generation measures of search quality.

Define engineering standards for end-to-end lifecycle of LLM-backed search quality measurement systems, from prototyping to reliable productionization at scale. Collaborate with ML partners to implement this lifecycle.

Work with machine learning engineers to advance the internal state of the art of AI model capabilities, data scale, and data freshness.

Platformize quality measurement systems to enable self-service operations by stakeholders and scale the team.

Basic Qualifications:

5+ years of engineering experience spanning backend and/or data systems. Either deep in one domain or balanced across both.

Demonstrated experience translating cross-team technical and product goals into detailed design and project plans, and in mentoring other engineers.

Experience operating with stateful and/or pipeline-based systems at high scale, with demonstrated impact reflecting a thoughtful design orientation and an understanding of the tradeoffs in data-intensive systems.

Preferred Qualifications:

Deep experience across both backend and data systems.

Knowledge of statistics (e.g., sampling techniques) and/or information retrieval quality assessment techniques.

Experience with Search technologies, e.g., OpenSearch, Elasticsearch, or other Lucene-based systems.

Experience with ML data preparation, training, and prediction pipelines.