✦ Luna Orbit — AI & Machine Learning

Lead Machine Learning Engineer - Personalization (ML Ops, Python, TensorFlow, FastAPI)

at Target

📍 7000 Target Pkwy N,NCD-0375 Brooklyn Park,MN 55445 Hybrid 💰 $132K – $238K USD / year Posted March 13, 2026
Salary $132K – $238K USD / year
Type Not Specified
Experience lead
Exp. Years 5+ years
Education Not specified
Category AI & Machine Learning

This role involves designing and deploying scalable machine learning solutions for personalization in a retail environment, leveraging Python and popular ML frameworks.

  • Design and implement ML models
  • Optimize models for production
  • Build data pipelines
  • Develop APIs for ML services
  • Collaborate with data scientists and engineers

Technical scope includes Python programming, TensorFlow, FastAPI, ML model development, data pipelining, model optimization, and API deployment, with a focus on scalable ML systems.

The ideal candidate is a lead machine learning engineer with over 5 years of experience in developing end-to-end ML solutions using Python, TensorFlow, and FastAPI. They should have strong expertise in model optimization, data pipelines, and deploying scalable ML models in a production environment.

PythonMachine LearningTensorFlowFastAPI5+ years experience
PyTorchXGBoostsklearnONNX
TensorFlowPyTorchXGBoostscikit-learnONNXFastAPI
PythonTensorFlowFastAPIML frameworksData pipeliningModel optimizationAPI designPyTorchXGBoostsklearnONNX
PythonTensorFlowFastAPIML frameworksMachine LearningData pipeliningModel optimizationAPI designPyTorchXGBoostsklearnONNX
software designcode reviewdocumentationtrainingpresentationbusiness understanding
Industry Retail/E-commerce
Job Function Lead development and deployment of machine learning models for personalization
PythonTensorFlowFastAPIPyTorchXGBoostsklearnscikit-learnONNXMachine LearningML frameworksData pipeliningModel optimizationAPI designDeep LearningAIML Ops

Less than 5 years of experience in ML engineering, Lack of proficiency in Python, No experience with TensorFlow or FastAPI

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