# Senior Machine Learning Operations Engineer
## About the Role
The Athletic Media Company is seeking a Senior Machine Learning Operations Engineer to help productionize and maintain advanced machine learning models. This role sits at the intersection of data science, data engineering, and infrastructure, with responsibility for building the systems that bring machine learning models into production and keep them reliable over time.
As the first Machine Learning Operations Engineer on the company’s small data science team, you will have a critical role in establishing the team’s ML Ops environment and best practices. You’ll work closely with data science and data engineering teams to streamline model training, validation, and deployment while building robust monitoring for model performance, drift, and data quality.
## Key Responsibilities
- Design, build, and maintain infrastructure for productionizing machine learning models.
- Develop infrastructure supporting highly visible product features at The Athletic.
- Work with the data science team to streamline model training, validation, and deployment.
- Implement robust monitoring and alerting for model performance, model drift, and data quality.
- Help ensure machine learning models remain accurate and reliable in production.
- Champion ML Ops best practices across the data science environment.
- Evaluate and integrate new technologies into the data science technology stack.
- Work closely with data science and data engineering teams to improve machine learning production workflows.
- Drive projects with minimal guidance and prioritize high-impact work.
- Build cross-functional relationships and communicate technical concepts to diverse audiences.
- Explain sophisticated concepts clearly and use data to craft compelling stories.
## Required Qualifications
- 4–6 years of experience as a data scientist, data engineer, or machine learning engineer.
- Proven ability to build machine learning model pipelines and infrastructure.
- Strong working knowledge of Python.
- Strong working knowledge of SQL for pulling, transforming, and working with data.
- Proven experience with machine learning frameworks such as scikit-learn or PyTorch.
- Experience with cloud platforms such as GCP, AWS, or Azure.
- Hands-on experience with Docker, Kubernetes, and Airflow.
- Ability to drive projects with minimal guidance.
- Ability to prioritize high-impact work.
- Strong verbal and written communication skills.
- Ability to build cross-functional relationships.
- Ability to explain sophisticated concepts to diverse audiences.
## Preferred Qualifications
The source job description does not identify any separate preferred or nice-to-have qualifications.
## Skills & Competencies
- Machine learning operations (ML Ops).
- Machine learning model productionization.
- Machine learning pipelines.
- Model training, validation, and deployment.
- Model monitoring and alerting.
- Model performance and drift monitoring.
- Data quality monitoring.
- Python.
- SQL.
- scikit-learn.
- PyTorch.
- Cloud platforms, including GCP, AWS, and Azure.
- Docker.
- Kubernetes.
- Airflow.
- Data engineering and data pipelines.
- Machine learning infrastructure.
- Problem-solving and prioritization.
- Independent project execution.
- Cross-functional collaboration.
- Technical communication.
- Data storytelling.
- Ability to communicate sophisticated technical concepts to diverse audiences.
## Education & Experience
**Education**
- No specific educational requirement is stated in the source job description.
**Experience**
- 4–6 years of experience as a data scientist, data engineer, or machine learning engineer.
- Proven experience building machine learning model pipelines and infrastructure.
- Experience with ML frameworks such as scikit-learn or PyTorch.
- Experience with cloud platforms such as GCP, AWS, or Azure.
- Hands-on experience with Docker, Kubernetes, and Airflow.
## Work Arrangement & Schedule
- **Work arrangement:** 100% remote.
- **Eligible locations:** United States or Canada.
- **Job listing location:** Alexandria, VA, US (Remote).
- **Employment type:** Not explicitly stated in the source job description.
- **Expected weekly hours:** Not specified.
- **Schedule flexibility:** Not specified.
- **Weekend requirements:** Not specified.
## Compensation & Benefits
- **Annual base salary:** $140,000–$150,000 USD.
- Total compensation may vary based on education, experience, skills, and location and may include non-cash rewards and benefits.
- The base salary range is subject to change.
- Employer-contributed medical, dental, vision, basic life, and disability insurance plans for U.S. full-time its recruiters use the company's official email domain, do not conduct interviews via text or instant message, and do not ask candidates to purchase equipment, download software, or provide sensitive employees.
- Savings accounts for medical, wellness, and childcare expenses.
- 401(k) retirement savings plan and employer match.
- Paid sick leave.
- 12 paid holidays.
- 15 days of accrued vacation to start.
- Up to 20 weeks of Paid Parental Leave.
- International candidates receive global benefits packages with similar benefits and perks competitive with their local market.
## Compliance / Additional Information
The Athletic Media Company is an equal opportunity employer and encourages people from all backgrounds and experiences to apply. The company considers applicants without regard to race, religion, color, national origin, ancestry, physical and/or mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, transgender status, age, sexual orientation, military or veteran status, or any other protected characteristic under applicable law.
The Athletic maintains an Applicant Privacy Notice describing how and when it collects, uses, and shares certain personal information of job applicants and prospective employees.
Candidates should be aware of fraudulent recruiting schemes. The Athletic states that its recruiters use the company's official email domain, do not conduct interviews via text or instant message, and do not ask candidates to purchase equipment, download software, or provide sensitive personally identifiable information such as bank account or Social Security information.