T

Target

Sells everyday goods and affordable designer styles

Senior Engineer - Multiple Teams

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeBrooklyn Park, MN, USA$98k - $176k

About the Job

Requirements

  • A four-year degree or equivalent experience is required.
  • At least 5 years of software development experience with strong hands-on engineering and programming skills are required.
  • Experience designing, building, testing, deploying, and operating production-grade applications, platform services, or distributed systems is required.
  • Experience with information retrieval, search architecture, retrieval-augmented generation, embeddings, vector databases, or related context and knowledge technologies is required.
  • Strong proficiency in one or more modern programming languages and experience developing application programming interfaces, services, and data-intensive systems are required.
  • Experience building or integrating semantic search, hybrid search, retrieval services, indexing pipelines, or enterprise search capabilities is required.
  • Understanding of memory systems, context management, grounding, and retrieval patterns for large language model-powered or agentic applications is required.
  • Strong understanding of distributed systems, stateful services, scalability, reliability, performance, security, and production operations is required.
  • Experience with Kubernetes, containers, continuous integration and continuous delivery, observability, testing, debugging, and operational monitoring is required.
  • Experience with search infrastructure, distributed indexing, metadata systems, knowledge ingestion, or enterprise data connectors is required.
  • Ability to translate ambiguous requirements and AI system context needs into concrete technical designs and working software is required.
  • Ability to evaluate retrieval and system tradeoffs involving relevance, latency, freshness, scale, reliability, and cost is required.
  • Strong problem-solving skills and ability to diagnose complex retrieval, indexing, and operational issues and prevent their recurrence are required.
  • Experience collaborating across platform, product, application engineering, architecture, and data teams is required.
  • Ability to communicate complex technical concepts, influence design decisions, mentor engineers, and raise engineering standards within a team is required.
  • Ability to maintain technical knowledge and stay current with evolving retrieval, knowledge, artificial intelligence, and platform technologies through formal training and self-directed learning is required.

Responsibilities

  • Design, build, and operate scalable retrieval and context services that enable artificial intelligence applications and agents to access trusted enterprise knowledge.
  • Build semantic, hybrid, and enterprise search capabilities using information retrieval techniques, embeddings, vector databases, indexing, and search infrastructure.
  • Develop retrieval-augmented generation and grounding patterns that improve the relevance, quality, and trustworthiness of artificial intelligence responses.
  • Build memory and context-management capabilities for conversational and agentic systems, including short- and long-term context and agent memory patterns.
  • Design and implement enterprise knowledge ingestion, content indexing, metadata, synchronization, and connector capabilities that keep knowledge accessible and fresh.
  • Build context orchestration services that select, assemble, and optimize the information supplied to artificial intelligence applications and agents.
  • Develop retrieval observability and diagnostics, including grounding quality, retrieval quality, search metrics, freshness, performance, and operational health.
  • Apply distributed systems and platform engineering principles to build reliable, scalable, secure, and maintainable stateful services and indexing systems.
  • Design and operate containerized services on Kubernetes and contribute to resilient, multi-region architectures for enterprise-scale context and knowledge workloads.
  • Evaluate retrieval, retrieval-augmented generation, GraphRAG, knowledge graph, semantic technology, and enterprise search approaches through research and proof-of-concept activities.
  • Lead code reviews, design reviews, testing, debugging, performance optimization, and architecture discussions while maintaining engineering and operational standards.
  • Partner with artificial intelligence platform, application engineering, product, architecture, and data teams to understand context needs and translate them into reusable platform capabilities.
  • Diagnose complex retrieval, indexing, grounding, memory, and production issues, identify systemic causes, and develop solutions that prevent repeat occurrences.
  • Share technical patterns and best practices and help engineering teams adopt Context Intelligence platform capabilities.

About the Company

Target

Sells everyday goods and affordable designer styles

Target sells groceries, clothing, home goods and other everyday products to U.S. shoppers through stores and its website, with an emphasis on affordable style and design. Its own brands include Cat & Jack children's clothing and Good & Gather food, alongside national brands and exclusive designer collections. Customers shop in person or order online for shipping, pickup or delivery, while Target Plus adds products from outside sellers. Merchandise sales drive its revenue, supplemented by Roundel advertising, marketplace fees and paid memberships. The company traces its roots to 1902, opened its first Target store in 1962, and is headquartered in Minneapolis, Minnesota, United States.

Company size
10,001+
Company stage
Not listed
Total funding
Not listed
Headquarters
Minneapolis, MN
Founded
1902

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