Senior AI Engineer-MarTech

Role Overview

The Senior AI Engineer will help build and scale production-grade AI/ML capabilities that power Target’s promo optimization and personalized marketing ecosystem. The team works at the intersection of AI engineering, machine learning, operations research, experimentation, marketing science, and production platform development to optimize promotional investments while improving guest relevance and business outcomes.

What You Will Do

Build production-grade AI/ML applications, design and develop scalable data and ML pipelines, implement end-to-end model training, evaluation, deployment, inference, monitoring, and lifecycle management workflows, and partner with Data Scientists to convert prototypes into reliable, reusable, and production-ready systems.

Why It Might Be a Fit

The ideal candidate will have strong software engineering fundamentals, practical AI/ML deployment experience, and the ability to balance innovation with reliability, scalability, security, and maintainability. If you enjoy solving complex problems, building enterprise-grade AI platforms, and shaping the future of AI-powered retail decisioning, this is a great opportunity to make meaningful impact at Target.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Science, Machine Learning, Mathematics, Statistics, or a related technical field, or equivalent practical experience.
• 4+ years of experience in software engineering, AI engineering, machine learning engineering, data engineering, MLOps, or production ML systems.
• Strong hands-on programming experience in Python, with the ability to write modular, maintainable, well-tested, production-quality code.
• Experience building and deploying end-to-end AI/ML pipelines, including data preparation, feature engineering, model training, model evaluation, model deployment, inference, monitoring, and lifecycle management.
• Strong understanding of MLOps practices, including CI/CD for ML, model versioning, experiment tracking, automated validation, model registry, retraining workflows, deployment automation, and production monitoring.
• Experience designing and operating scalable model inference systems, batch scoring pipelines, APIs, microservices, or event-driven ML integrations.
• Experience working with distributed data processing systems such as Spark, Hadoop/Hive, or equivalent large-scale data platforms.
• Experience with SQL and one or more database technologies, including relational databases, NoSQL databases, object stores, or feature stores.
• Strong software engineering fundamentals, including data structures, algorithms, system design, API design, testing, code reviews, error handling, debugging, and documentation.
• Working knowledge of machine learning concepts, model evaluation, feature engineering, model serving, and common ML frameworks.
• Experience with containerization, orchestration, cloud platforms, workflow schedulers, and modern DevOps practices.
• Good understanding of observability and reliability for AI/ML systems, including monitoring, alerting, logging, performance tracking, debugging, and root-cause analysis.
• Ability to partner effectively with Data Scientists and translate experimental models or notebooks into scalable production systems.
• Ability to work in ambiguous problem spaces, break down complex systems, and deliver high-quality solutions against business timelines.
• Excellent written and verbal communication skills, with the ability to explain technical concepts, trade-offs, and system behavior to both technical and non-technical audiences.

Benefits

• Dental insurance
• Vision insurance
• Health insurance
• 401(k) plan
• Paid time off
• Paid holidays
• Flexible work arrangements
• Professional development opportunities
• Employee discounts
• Stock options
• Relocation assistance
• Visa sponsorship