Machine Learning Engineer I

Condé Nast is a global media company producing the highest quality content with a footprint of more than 1 billion consumers in 32 territories through print, digital, video and social platforms. The company’s portfolio includes many of the world’s most respected and influential media properties including Vogue, Vanity Fair, Glamour, Self, GQ, The New Yorker, Condé Nast Traveler/Traveller, Allure, AD, Bon Appétit and Wired, among others.

Job Description

Location

Chennai, TN

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 company's 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 work—you 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

• 2–4 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 ap