Machine Learning Engineer, Personalization

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

The machine learning effort is part of the Data Science team at Teladoc Health and works closely with product managers, engineering, and operations to identify, build, ship, and maintain machine learning models and pipelines to personalize how our members manage chronic conditions.

This is an opportunity to use technical rigor to apply machine learning to real-world business problems, and engineer, deploy, measure, and iterate ML in production, for every Teladoc Health member.

Responsibilities

• Design, develop, deploy, and maintain production-grade data processing, machine learning, and deep learning code, pipelines, and systems
• Contribute to the MLOps infrastructure improvement efforts to scale the deployment and utilization of ML models in various stages of our member engagement and retention
• Use Python, SQL, Tensorflow, and PyTorch to run exploratory analyses using both traditional and deep-learning techniques. This includes feature engineering, unsupervised learning, supervised learning, reinforcement learning
• Run machine learning tests and experiments; measure the performance of deployed models; work with data scientists to propose improvements and iterate to improve performance
• Manage machine learning model lifecycle: develop, deploy, monitor, maintain, and update models in production
• Research and implement appropriate ML algorithms and tools
• Select appropriate datasets and data representation methods
• Extend existing ML libraries and frameworks
• Keep abreast of developments in the field
• Collaborate closely with product management and engineering teams to drive requirements and define new product features and architecture

Candidate Profile

• Extensive experience in building and scaling maintainable software, data-engineering, and machine learning pipelines
• Ability to write robust code in Python, Spark, SQL
• Proven experience as a Machine Learning Engineer or a similar role
• Experience designing and building reusable, maintainable code for model training, evaluation, and interpretation
• Advanced degree in computer science, math, statistics, or a related discipline
• Deep knowledge of probability, statistics, and algorithms
• Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
• Familiarity with ML systems, recommendation engines, NLP, etc.
• Experience with agile sprint processes to deliver ML work
• Familiarity with the entire ML algorithm lifecycle (modeling approach ideation, POC, data exploration, data processing, feature extraction, feature construction, model development, evaluation, iteration)
• Willingness to learn new ML platforms and tools, as well as propose and help teams adopt new tools
• Great active listening skills to infer product needs and underlying context
• Ability to collaborate effectively with peers, and respect for member privacy