Theathletic3mo ago

Senior Machine Learning Operations Engineer (Remote)

United StatesRemoteFull-timesenior

Data ScienceOtherMachine Learning EngineerMachine Learning Operations EngineerData & AI

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Quick Summary

Overview

About Us Powered by one of the largest global newsrooms in sports media, The Athletic brings sports fans the most comprehensive stories about the teams, sports and athletes they love.

Technical Tools

airflowawsazuredockergcpkubernetespythonpytorchscikit-learnsqletlmachine-learning

About Us

Powered by one of the largest global newsrooms in sports media, The Athletic brings sports fans the most comprehensive stories about the teams, sports and athletes they love. The Athletic’s newsroom of 500+ full-time staff delivers in-depth coverage of hundreds of professional and college teams across more than 47 North American markets and all 20 football clubs in the English Premier League, as well as many Championship clubs.

About the Role

We are looking for a senior machine learning operations engineer who understands data pipelines, infrastructure, and model monitoring. In this role, you will work closely with our data science and data engineering teams to help us productionize and maintain advanced machine learning models. As the first machine learning operations engineer on our small data science team, your work will be critical to both the data team at The Athletic and the company as a whole.

This role is 100% remote for candidates currently residing in either the United States or Canada.

Design, build, and maintain machine learning model productionization infrastructure. These models will drive very visible product features at The Athletic, and your infrastructure is key to their success.

Work with the rest of the data science team to streamline model training, validation, and deployment.

Implement robust monitoring and alerting for model performance, drift, and data quality, ensuring our models remain accurate in production.

Cultivate an outstanding data science environment by championing best practices in ML Ops and evaluating and integrating new technologies into our data science stack.

4-6 years of experience as a data scientist, data engineer, or machine learning engineer with a proven ability to build machine learning model pipelines and infrastructure.

Strong working knowledge of Python and SQL to pull, transform, and otherwise deal with data in all of its forms.

Proven experience with ML frameworks (e.g., scikit-learn, PyTorch) and cloud platforms (e.g., GCP, AWS, Azure).

Hands-on experience with Docker, Kubernetes, and Airflow.

Ability to drive projects with minimal guidance and prioritize high-impact work.

Strong verbal and written communication skills with ability to build cross-functional relationships.

Can explain sophisticated concepts to diverse audiences and craft compelling stories with data.