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

Target Target · Retail · NCD-0375 Brooklyn Park, MN

Sr. Machine Learning Engineer focused on MLOps for marketing and corporate systems at Target. This role involves implementing, deploying, and optimizing ML solutions in production, with a focus on personalization and audience creation. Responsibilities include data pipelining, model optimization, deployment, API design, CI/CD pipelines, and collaboration with cross-functional teams.

What you'd actually do

  1. implementing solutions that create and maintain audiences for highly personalized offers to our Guests
  2. designing, implementing, and optimizing the machine learning solutions in production
  3. understand best-practice software design, participate in code reviews, create a maintainable and well-tested codebase with relevant documentation
  4. 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
  5. Work in partnership with applied data scientists, software engineers and product managers to understand the business requirements - translate to machine learning solutions at scale

Skills

Required

  • Python
  • ML frameworks (Pytorch, TensorFlow, xgboost, sklearn, ONNX)
  • Cloud ML services (GCP Vertex AI, Azure ML, Sagemaker)
  • Distributed training frameworks (Spark, Ray, TensorFlow Distributed)
  • Serving frameworks (TorchServe/TensorFlow Serving, FastAPI)
  • Big Data technologies (Hadoop ecosystem, Spark, Kafka, Hive)
  • CI/CD pipelines for model deployment
  • End-to-end Machine Learning application development
  • Model optimization
  • Deployment
  • API design

Nice to have

  • MS in Computer Science, Applied Mathematics, Statistics, Physics or equivalent work or industry experience

What the JD emphasized

  • 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

Other signals

  • implementing solutions that create and maintain audiences for highly personalized offers
  • designing, implementing, and optimizing the machine learning solutions in production
  • deploying Machine Learning algorithms into production environments
  • creating and maintaining CI/CD pipelines for automated model deployment and testing