Job Description

Location:

About Company: Condé Nast is a global media company home to iconic brands including Vogue The New Yorker GQ Glamour AD Vanity Fair and Wired among many others. The companys award-winning content reaches 84 million consumers in print 367 million in digital and 379 million across social platforms and generates more than 1 billion video views each month.

The company is headquartered in London and New York and operates in 31 markets worldwide including China France Germany India Italy Japan Mexico & Latin America Russia Spain Taiwan the U.K. and the U.S. with local licensee partners across the globe.

Job Summary
Condé Nast is looking for a Machine Learning Engineer I to play a key role in building and operating our recommendations platform. This role goes beyond productionizing data science workyou will take end-to-end ownership of ML-powered systems from design to deployment to continuous improvement.
You will work as an equal partner with Data Scientists to shape solutions define scalable architectures and ensure reliable high-performance ML systems in production. This is an ideal role for an engineer who thrives on ownership can quickly understand complex systems and is motivated to build and evolve production-grade ML platforms.

Key Responsibilities
Own and manage production ML pipelines and workflows ensuring reliability
scalability and performance.
Design build and continuously improve systems powering personalized
recommendations and related use cases.
Collaborate with Data Scientists as a peer to co-design ML solutions translating
business and modeling requirements into robust engineering systems.
Take full lifecycle ownership of ML systems: design development deployment
monitoring and iteration.
Build reusable frameworks and platforms that accelerate experimentation and
productionization of ML use cases.
Develop and optimize both batch and near-real-time data processing pipelines.
Implement and maintain CI/CD pipelines for ML workflows and data systems.
Proactively monitor debug and resolve production issues ensuring high system
reliability and data quality.
Improve existing pipelines by identifying bottlenecks reducing latency and optimizing cost and performance.
Contribute to architectural decisions and help define best practices for ML
engineering within the team.

Work in an agile environment with a strong focus on code quality testing and
incremental delivery.

Desired Skills & Qualifications
24 years of experience in software engineering data engineering or ML
engineering roles.
Strong proficiency in Python and experience with libraries such as PyTorch
scikit-learn Pandas NumPy and PySpark.
Solid understanding of software engineering principles data structures and system design.
Hands-on experience building and maintaining production data pipelines or ML
systems.
Experience with big data technologies such as Spark Kafka Hive or Hadoop.
Familiarity with Databricks or AWS (S3 EC2 IAM EMR SageMaker).
Experience designing workflows for large-scale data processing (batch or streaming).
Exposure to API development and serving ML models in production environments.
Working knowledge of Docker; familiarity with Kubernetes is a plus.
Experience implementing CI/CD pipelines for data or ML systems.
Strong debugging problem-solving and analytical skills.
Ability to quickly understand existing systems and take ownership with minimal
ramp-up time.
Good communication skills and ability to collaborate effectively across teams.
Preferred Qualifications
Experience with Airflow or Astronomer for workflow orchestration.
Familiarity with MLflow or similar tools for experiment tracking and model lifecycle management.
Exposure to real-time or near-real-time ML use cases.
Experience working on recommendation systems or personalization platforms.

What happens next

If you are interested in this opportunity please apply below and we will review your application as soon as possible. You can update your resume or upload a cover letter at any time by accessing your candidate profile.

Condé Nast is an equal opportunity employer. We evaluate qualified applicants without regard to race color religion sex sexual orientation gender identity national origin disability veteran status age familial status and other legally protected characteristics.