Working at Target means helping all families discover the joy of everyday life. That purpose drives everything we build—especially our technology. Our teams create scalable, reliable systems that power meaningful guest and team member experiences.
About the Role As an Engineer, you’ll build the technology behind our products—focusing on scalable backend systems, modern engineering practices, and emerging AI capabilities. You’ll work closely with your team to design, build, and improve services, while balancing speed, quality, and reliability. You’ll also contribute to code quality, system design, and day-to-day operations with a strong sense of ownership.
What You’ll Do
- Build and improve backend services and microservices
- Work on distributed systems and data pipelines
- Design and develop APIs; contribute to full-stack integrations when needed
- Write clean, maintainable, and production-ready code
- Monitor systems, troubleshoot issues, and handle production support
- Participate in code reviews and design discussions to improve quality
- Explore and help evolve the GenAI/ML capabilities in applications
What We’re Looking For
Basic Qualifications
- Bachelor’s degree in Computer Science or equivalent experience
- 2–4 years of relevant software engineering experience
Must-Have (Core Skills)
Strong programming skills in one language (Python, Java, Kotlin, or JavaScript/TypeScript)
Solid understanding of:
- APIs and microservices
- Data structures and problem-solving
- Basic system design and distributed systems concepts
Experience with modern development practices (CI/CD, version control, testing)
Ability to independently own and deliver features end-to-end
Willingness to learn, adapt, and grow in a fast-paced environment
Good-to-Have (Differentiators)
- Experience with distributed data systems (Kafka, Spark, etc.)
- Hands-on with Docker, Kubernetes, or infrastructure-as-code tools
- Experience with SQL or NoSQL databases
- Exposure to frontend technologies (React, TypeScript, etc.)
- Experience working on production systems at scale
AI / ML Exposure (a strong plus):
- Working with LLM APIs, prompt engineering
- Understanding of RAG, embeddings, or vector databases
- Familiarity with ML tools (MLflow, Airflow, etc.)
- Exposure to agent-based frameworks (LangChain, AutoGen, etc.)