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

As a Senior Machine Learning Engineer within Target Data Sciences, you will design, implement, and deploy ML solutions to build and optimize audiences for highly personalized offers, collaborating across product, engineering, marketing, and analytics teams.

Responsibilities

• Partner with product, engineering, marketing, and analytics to define strategy, lead experiments, and ensure personalization yields measurable impact for guests and the business.
• Design, implement, and optimize machine learning solutions in production environments.
• Apply best practices in software design, participate in code reviews, and maintain a maintainable, well-tested codebase with relevant documentation.
• Lead training sessions and present work to both technical and non-technical audiences, converting business priorities into requirements and scalable solutions.
• Be part of a Data Sciences team focused on creating and maintaining audiences for highly personalized guest offers.

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
• 3+ 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 solutions at scale
• 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 tight timelines
• Motivated, team player with ability to collaborate effectively across global team

Technologies

• Python
• Pytorch
• TensorFlow
• xgboost
• sklearn
• ONNX
• GCP Vertex AI
• Azure ML
• Sagemaker
• Spark
• Ray
• TensorFlow Distributed
• TorchServe
• TensorFlow Serving
• FastAPI
• Hadoop
• Kafka
• Hive
• CI/CD pipelines

Benefits

• Health benefits (medical, vision, dental, life insurance)
• 401(k)
• Employee discount
• Short-term disability
• Long-term disability
• Paid sick leave
• Paid national holidays
• Paid vacation
• Education benefits

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+ 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 solutions at scale
• 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 tight timelines
• Motivated, team player with ability to collaborate effectively across global team