Sr Machine Learning Engineer

About the role

We are seeking a hands-on Senior Machine Learning Engineer to support and enhance machine learning platforms used for media measurement and customer analytics. This role partners closely with Data Scientists, Data Engineers, and Analytics stakeholders to deploy, maintain, and improve production ML workflows across AWS.

Responsibilities

• Support the deployment, monitoring, and ongoing maintenance of media measurement and customer modeling systems in partnership with Data Science and Engineering teams.
• Develop and maintain SageMaker processing and training jobs, model endpoints, and supporting infrastructure across development, testing, and production environments.
• Contribute to Step Functions, Lambda functions, and Airflow (MWAA) workflows that orchestrate model training, scoring, retraining, and analytics pipelines.
• Support MLflow model registration and promotion processes, configuration management, and versioned model artifacts.
• Build and maintain Docker images, ECR repositories, and GitLab CI/CD pipelines to enable reliable model deployment and release processes.
• Help productionize machine learning models and data pipelines that support customer analytics, scoring, and decisioning use cases.
• Investigate and resolve production issues using CloudWatch, DataDog, SageMaker logs, and workflow monitoring tools.
• Collaborate with cross-functional partners to implement platform enhancements, improve operational reliability, and deliver new capabilities.
• Contribute to engineering best practices, documentation, testing strategies, and operational procedures.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
• 3+ years of experience in Machine Learning Engineering, MLOps, Software Engineering, or related technical roles.
• Strong Python development skills, including experience with pandas, PyTorch, scikit-learn, boto3, and SQL.
• Experience working with AWS services such as SageMaker, Step Functions, Lambda, S3, IAM, and ECR.
• Experience developing or supporting orchestration workflows using Airflow, Glue, or similar technologies.
• Familiarity with cloud-based data platforms such as Snowflake, Redshift, or Athena.
• Experience with Docker, CI/CD pipelines, source control workflows, and software development best practices.
• Strong troubleshooting and debugging skills across distributed systems and machine learning workflows.
• Ability to collaborate effectively with technical and non-technical stakeholders.

Qualifications

• Required Qualifications
• Preferred Qualifications

Benefits

• Medical, dental, vision, legal, and accidental death and dismemberment insurance
• Short-term and long-term disability, life insurance
• 401(k) plan
• 4 weeks of vacation, paid sick leave, 10 paid holidays, a floating day off, and 2 paid days for volunteer time each calendar year