Machine Learning Engineer I

Cond Nast is seeking a motivated and skilled Machine Learning Engineer I to support the productionization of machine learning projects in Databricks or AWS environments for the Data Science team.

This role is ideal for an engineer with a strong foundation in software development, data engineering, and machine learning , who enjoys transforming data science prototypes into scalable, reliable production pipelines .

Note: This role focuses on deploying, optimizing, and operating ML models rather than building or researching new machine learning models.

Primary Responsibilities

• Build, optimize, and maintain data and ML pipelines to deploy machine learning models into production environments.
• Assist in transforming data science prototypes into reusable, production-ready engineering frameworks.
• Contribute to the design and implementation of scalable ML workflows processing large volumes of data.
• Support near-real-time and batch processing systems for ML use cases.
• Collaborate closely with Machine Learning Engineers and Data Scientists in designing and engineering ML solutions.
• Participate in the full development lifecycle , from design and implementation to testing and release.
• Implement and maintain CI/CD pipelines for ML models and data workflows.
• Proactively identify, debug, and resolve issues in ML pipelines and production jobs.
• Follow agile development practices with a focus on code quality, testing, and incremental delivery .
• Participate in quality assurance, testing, and defect resolution.

Desired Skills & Qualifications

• 2-4 years of software development experience involving machine learning or data-intensive systems .
• Strong proficiency in Python , with experience using libraries such as TensorFlow, PyTorch, scikit-learn, Pandas, NumPy, and PySpark .
• Good understanding of data structures, data modeling, and software engineering principles .
• Experience working with big data technologies such as Spark, Hadoop, Kafka, Hive, or AWS EMR .
• Exposure to Databricks or Amazon SageMaker for ML development or deployment.
• Experience building data pipelines and ML workflows in production or pre-production environments.
• Familiarity with API development and serving ML models as RESTful services.
• Experience working with Docker and basic exposure to Kubernetes is a plus.
• Experience with CI/CD pipelines for ML or data workflows.
• Good communication skills and ability to work effectively within a team.
• Strong analytical and problem-solving skills.
• Undergraduate or Postgraduate degree in Computer Science or a related discipline .

Preferred Qualifications

• Experience using Airflow, Astronomer, MLflow, or Kubeflow .
• Exposure to Spark, or PySpark in data processing systems.
• Familiarity with AWS services commonly used in ML pipelines (S3, EC2, IAM, etc).
• Experience with near-real-time data processing use cases.