Sr Machine Learning Engineer - Marketing and Corporate Systems (ML Ops)

The pay range is $98,000.00 - $176,000.00 Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves. Other benefits for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation. Find competitive benefits from financial and education to well-being and beyond at https://corporate.target.com/careers/benefits. JOIN TARGET AS A SR AI/ML ENGINEER - MARKETING TECH About Us: Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here. 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 a Sr AI/ML Engineer, you will join a Data Sciences team responsible for implementing solutions that create and maintain audiences for highly personalized offers to our Guests. You will collaborate with cross-functional partners in product, engineering, marketing, and analytics to define strategy, lead experimentation, and ensure that personalization drives measurable impact for our guests and business. You will play crucial role in designing, implementing, and optimizing the machine learning solutions in production. We will also expect you to understand best-practice software design, participate in code reviews, create a maintainable and well-tested codebase with relevant documentation. At an organizational level, you will 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. About you: 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 3 plus years' of experience in end-to-end Machine Learning application development including data pipelining, model optimization, deployment, and API design Experience deploying Machine Learning algorithms into production environments Highly proficient programming in Python Experience with ML frameworks such as Pytorch, TensorFlow, xgboost, sklearn and ONNX Extensive experience with one or more cloud ML service such as GCP Vertex AI, Azure ML or Sagemaker Experience using distributed training frameworks like Spark, Ray, TensorFlow Distributed Experience with serving frameworks such as TorchServe/TensorFlow or Serving/FastAPI Good understanding of Big Data tech, specifically Hadoop ecosystem – Spark, Kafka, Hive, etc. Experience creating and maintaining CI/CD pipelines for automated model deployment and testing Work in partnership with applied data scientists, software engineers and product managers to understand the business requirements - translate to machine learning sol