A Machine Learning Engineer is a crucial member of the Data Science team. You will be responsible for assisting our data scientists, date engineers, and dev ops engineers to deploy and scale customer-facing algorithms. This position is an opportunity to be part of a fast-growing and globally focused network of data scientists, machine learning engineers, and AI practitioners developing novel approaches to personalization and recommender engines. You will have also to have access to word class academics though our partnerships and joint contracts with universities, as well as our staff of PhDs and researchers.
Essential functions:
• Optimize the team’s algorithms for deployment
• Develop transportable and customizable SDKs for our core algorithms, allowing them to be plugged into any of our apps
• Develop SDKs for rapid prototyping and deployment of algorithms
• Automate and orchestrate advanced split tests of models
• Continually refine the department’s code base and ensure best development processes
• Identify constraints, strengths, and weaknesses of technology options for each algorithm
Skills:
• Expert in distributed computing with Spark, DevOps best practices, and containerization
• Expert coding ability in Python and high competency with PySpark
• Understanding of, and experience productionizing, the following ML concepts:
o Collaborative filtering
o Matrix factorization
o Ranking algorithms
o Advanced regression, classification, clustering, and time series algorithms
o Automated hyperparameter tuning and model selection
o Deep learning at scale for NLP
• Experience with advanced search concepts using deep learning is a plus!
Experience:
• 3 + years of applied Machine Learning experience
• 2 + years of experience deploying, scaling, monitoring, and continually improving algorithms
• Experience deploying models on very large data using:
o Spark, MLlib, PySpark, Scala
o Kafka
o Kubernetes, Docker, containerization
• Knowledge of model management
• Knowledge of API development best practices