Role overview
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.
What we're looking for
- 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 .
- 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.
Tags & focus areas
Used for matching and alerts on DevFound Manager Quality Assurance Data Science Data Modeling Analytical Machine Learning Data Structures Data Processing Aws Data Engineer Ai