Software Engineer II - Engineer

Software Engineer — Delivery Search

About the Role

We are seeking talented Software Engineers to join our Search Engineering team and help build the next generation of AI-powered search experiences across Uber Eats, Grocery, and Retail.

In this role, you will build and improve large-scale backend systems that power search retrieval, ranking, query understanding, and emerging LLM-based search experiences. You will work on high-throughput, low-latency services that retrieve and rank relevant restaurants, stores, dishes, and items from large catalogs in real time.

You will collaborate closely with backend and ML engineers, data scientists, product managers, and platform teams to improve search quality, latency, scalability, and reliability. You will have opportunities to work across traditional search infrastructure, semantic retrieval and machine learning, as well as emerging LLM-powered retrieval, ranking, and search experiences.

About the Team

The Delivery Search team builds the core search technology behind Uber Eats, Grocery, and Retail. We work across the search stack, including query understanding, indexing, lexical and semantic retrieval, ranking, model serving, and search serving infrastructure.

Our work combines large-scale distributed systems, information retrieval, machine learning, and increasingly LLMs and generative AI to solve challenging problems around relevance, latency, scalability, and reliability. The team owns systems serving millions of users and is evolving the search stack toward more intelligent, real-time, and AI-powered architectures.

Basic Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• 2+ years of professional software engineering experience building and delivering production systems.
• Proficiency in at least one programming language such as Go, Java, C++, or Python.
• Experience developing backend services, APIs, or distributed systems.
• Understanding of core computer science concepts, including data structures, algorithms, concurrency, and system design.
• Experience writing maintainable, well-tested production code.
• Strong problem-solving skills and ability to collaborate effectively with engineers and cross-functional partners.

Preferred Qualifications

• Experience building scalable, high-throughput, or low-latency backend systems.
• Familiarity with search, recommendation, information retrieval, ranking, or ML-powered systems.
• Familiarity with LLMs, generative AI, or ML model-serving systems and an interest in applying them to large-scale search.
• Experience with embeddings, semantic retrieval, approximate nearest-neighbor search, or vector search.
• Experience building or integrating LLM-powered applications, retrieval/ranking systems, or evaluation pipelines.
• Familiarity with model-serving or inference concepts such as batching, caching, streaming, or latency optimization.
• Experience with search technologies such as Elasticsearch, OpenSearch, Solr, Vespa, Lucene, or similar retrieval systems.
• Familiarity with distributed systems and technologies such as Kafka, Redis, Cassandra, Spark, or Flink.
• Experience with performance optimization, monitoring, observability, and production debugging.
• Experience working on large-scale consumer-facing products.

What the Candidate Will Do

• Build and maintain scalable backend services powering search across Uber Eats, Grocery, and Retail.
• Develop retrieval and ranking systems that help customers find relevant restaurants, stores, dishes, and items.
• Build and productionize LLM- and ML-powered search capabilities, including query understanding, retrieval, ranking, and emerging generative search experiences.
• Work with lexical retrieval, semantic retrieval, embeddings, and ML/LLM-based ranking to improve search relevance.
• Partner with ML engineers and data scientists to integrate machine-learned and LLM-based models into production search serving paths.
• Build scalable search serving and indexing infrastructure capable of handling high query volumes and large catalogs.
• Improve system latency, throughput, reliability, and infrastructure efficiency across retrieval, ranking, model serving, and data hydration.
• Contribute to infrastructure supporting efficient model inference, caching, batching, and streaming for latency-sensitive search experiences.
• Analyze search and system performance, debug production issues, and identify opportunities to improve relevance and serving performance.
• Build observability and reliability mechanisms and participate in the operational ownership of production search systems.
• Participate in design and code reviews and contribute to technical decisions for well-scoped projects.
• Write clean, reliable, well-tested, and maintainable production code.
• Collaborate with Product, Data Science, ML, and other engineering teams to ship customer-facing search improvements.