Ai/ml Engineer - Recsys (ml Ops)

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

AI/ML Engineer role focused on building and optimizing production machine learning solutions for personalized recommendations on Target.com and the Target App. Responsibilities include designing, implementing, and optimizing ML solutions and their supporting platforms, with a focus on ML Ops, data pipelining, and model deployment. Requires strong programming skills in Python, Java, SQL, experience with ML frameworks, cloud ML services, and containerized technologies.

What you'd actually do

  1. designing, implementing, and optimizing production machine learning solutions and the platforms supporting them
  2. understand best practice software design, participate in code reviews, and create a maintainable well-tested codebase with relevant documentation
  3. work in partnership with data scientists, engineers, and product managers to understand the business requirements and build solutions to meet business needs

Skills

Required

  • BS, MS or PhD in Computer Science, Applied Mathematics, Statistics, Physics, equivalent work or industry experience
  • 1 plus year of applied machine learning application development including data pipelining, model optimization, deployment/ML Ops and API design
  • Strong programming in Python, Java, SQL
  • experience with ML frameworks such as PyTorch, TensorFlow, XGBoost and sklearn
  • Experience with cloud ML services such as Vertex AI, Azure ML or AWS Sagemaker
  • Experience with containerized technologies like Docker and Kubernetes
  • Experience with PySpark
  • Experience with software design on Linux/Mac
  • Experience with software version control such as Git and software test coverage practices/frameworks such as PyTest or JUnit
  • Excellent communication skills with the ability to clearly tell data driven stories through appropriate visualizations, graphs and narratives
  • Self-driven and results oriented; able to meet deadlines
  • Motivated, team player with ability to collaborate effectively across a global team

What the JD emphasized

  • 1 plus year of applied machine learning application development including data pipelining, model optimization, deployment/ML Ops and API design

Other signals

  • production machine learning solutions
  • platforms supporting them
  • model performance at scale