Senior Machine Learning DevOps Engineer - US

Associate / Junior



Meytier Premier Employer

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About This Workplace

Meytier Partner

In this role, you’ll join our growing team of world-class engineers and statisticians to build and maintain our machine learning architecture. You will be tasked with finding the most promising opportunities for impact and then delivering on them.

Our ML team’s work is focused in the following four areas:

  • Information retrieval from unstructured clinical text
  • Predicting clinical findings from structured and unstructured data sources
  • Building intelligent algorithms for ordering work queues
  • Identifying anomalies, such as behavior change and gaming, in user behavior
  • Learning effective personalized treatment paths that enable care recommendations based on socio economic status, care availability and patient risk


  • Establish software engineering best practices for machine learning model deployments, including CI/CD, automation, modularization, automated testing, and monitoring
  • Develop and deploy scalable tools and services to manage machine learning training, inference, model versioning, model promotion/demotion, and model performance evaluation/tracking
  • Identify and evaluate new technologies to improve performance, maintainability, scalability and reliability of our production machine learning systems
  • Administer resource configuration, access, and alerts to ensure appropriate use of computational resources for model development and deployment

Desired Profiles:

  • You have 5+ years experience in applied DevOps for Machine Learning or Data Science teams in industry with a Bachelor’s degree in computer science, machine learning, software engineering or similar field
  • Experience with model deployment, model maintenance and the methods to evaluate models in product use
  • Expert in Python and Spark/pySpark
  • Experience building end-to-end systems as a Platform Engineer, ML Engineer, Data Scientist or Data Engineer
  • Experience developing and maintaining ML systems built with open source tools
  • Strong understanding of software testing, benchmarking, and continuous integration
  • Hands on experience using AWS tools and ElasticSearch
  • You possess proven experience across a range of DevOps capabilities such as log aggregation, CI/CD, Linux, Docker and Kubernetes

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