The Athletic Media Company - Senior Machine Learning Operations Engineer
Requirements
• 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. • The annual base salary range for this role is $140,000.00 - $150,000.00 USD. The total compensation offered for this position may vary based on factors such as education, experience, skills, and location. It may also include non-cash rewards and benefits. The base salary range is subject to change and may be modified in the future. • The Athletic offers unique perks and benefits to all full-time employees based on their country of residence. Our comprehensive US benefits package includes: • Highly competitive, employer-contributed medical, dental, vision, basic life and disability insurance plans. • Savings accounts for medical, wellness, and childcare expenses. • 401k retirement savings plan and employer match. • Paid time off including paid sick leave, 12 paid holidays, 15 days of accrued vacation to start, and up to 20 weeks of Paid Parental Leave. • For international candidates: Our global benefits packages offer similar benefits and perks, competitive to the local market.
Responsibilities
• 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.
Benefits
• Equity: Explicitly stated as part of the benefits package. • Remote Work Options: Explicitly stated as a benefit for the position being remote.
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