Job description

To be successful, you need a strong machine learning background, proven software development skills, a love of learning, and to collaborate with cross-functional teams, including researchers, engineers, data scientists/analysts, and product managers, to develop and implement machine learning algorithms. You’ll mentor other MLE’s and lead an effort to build scalable end-to-end machine learning solutions for our retail customers Proficiency in one or more object-oriented programming languages such as Python, Java, C++ and experience building highly scalable distributed systems Hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (eg: Spark, SQL, Snowflake/Hadoop, etc) Bachelors in a quantitative field, such as Computer Science, Applied Mathematics, Statistics, or Bachelors degree in quantitative field with a focus on AI in coursework Understanding of machine learning model lifecycle from prototyping, feature engineering, training, inference, deployment, monitoring and continuous improvements via deep analysis) Experience in Recommender Systems, Personalization, Search, Computational Advertising or Natural Language Processing including RAG based Generative AI and transformer architecture Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning in real applications a plus Experience with Spark, TensorFlow, Keras, and PyTorch a plus Skilled in communication, problem solving, strategic thinking

Requirements

Must-haves

Proficiency in one or more object-oriented programming languages such as Python, Java, C++ and experience building highly scalable distributed systems
Hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (eg: Spark, SQL, Snowflake/Hadoop, etc)
Bachelors in a quantitative field, such as Computer Science, Applied Mathematics, Statistics, or Bachelors degree in quantitative field with a focus on AI in coursework
Understanding of machine learning model lifecycle from prototyping, feature engineering, training, inference, deployment, monitoring and continuous improvements via deep analysis)
Experience in Recommender Systems, Personalization, Search, Computational Advertising or Natural Language Processing including RAG based Generative AI and transformer architecture
Skilled in communication, problem solving, strategic thinking

Nice-to-haves

Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning in real applications a plus
Experience with Spark, TensorFlow, Keras, and PyTorch a plus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningpythonjavasqlai

Platform: apple
Effort: medium