Lead Machine Learning Engineer - Merchandising AI (ML Ops)

Target•Brooklyn Park, MN

1d•$132,000 - $238,000•Hybrid

About The Position

Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. A role with Target Data Sciences means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Applied Data Sciences or Machine Learning teams, you’ll be challenged to harness Target’s impressive data breadth to build the algorithms that power solutions our partners in Digital Marketing, Supply Chain Optimization, Advanced AI, Search and Personalization rely on. Every Scientist on Target’s Data Sciences team can expect modeling and data science, software/product development of highly performant code for model performance at scale. As the Lead Machine Learning Engineer – Merchandising, you will join a Data Sciences team responsible for creating and optimizing the data and models used to build a world-class merchandising product. You will play a crucial role in designing, implementing, and optimizing production machine learning solutions. We will also expect you to understand best practice software design, participate in code reviews, and create a maintainable well-tested codebase with relevant documentation. At an organizational level, you may conduct training sessions, present work to technical and non-technical peers/leaders, build knowledge on business priorities/strategic goals and leverage this knowledge while building requirements and solutions for each business need. Core responsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs.

Requirements

4-year degree in Quantitative disciplines (Science, Technology, Engineering, Mathematics) or equivalent experience
MS in Computer Science, Applied Mathematics, Statistics, Physics or equivalent work or industry experience in applied machine learning
5 plus years of experience in end-to-end machine learning application development, including data pipelining, model optimization, deployment and API design
Extensive experience with applied machine learning frameworks and the ability to developing highly distributed machine learning systems at scale
Highly proficient in Python programming
Strong understanding of CI/CD practices for machine learning systems and ML Ops principles
Extensive experience with Google Cloud’s Vertex AI and/or the broader cloud-based ML ecosystem
Demonstrated knowledge of various testing frameworks and containerization like Docker and Kubernetes
Demonstrated experience in REST API design and development
Extensive understanding of Big Data technologies including Hadoop, Spark and Kafka
Proven ability to collaborate with data scientists, software engineers and product managers to deliver scalable machine learning solutions
Excellent communication skills with an ability to tell data-driven stories through visualizations and narratives
Ability to mentor engineers and help establish engineering standards, architecture patterns, and best practices for AI/ML and cloud-native development
Self-driven, results-oriented and able to meet tight deadlines
Motivated team player who thrives in a collaborative global environment

Responsibilities

Lead the development of agentic AI solutions that connect data, tools, APIs and business rules into end-to-end decisioning workflows
Understand best practice software design, participate in code reviews, and create a maintainable well-tested codebase with relevant documentation
Conduct training sessions, present work to technical and non-technical peers/leaders, build knowledge on business priorities/strategic goals and leverage this knowledge while building requirements and solutions for each business need