Machine Learning Engineer
Condé Nast is a premier media company renowned for producing the highest quality content for the world’s most influential audiences, attracting more than 100 million consumers across its industry-leading print, digital, and video brands.
Condé Nast is home to many of the world’s most-celebrated magazine and website brands. The company’s reputation for excellence results from our commitment to publishing the best consumer, trade, and lifestyle content. Our brands include Vogue, Epicurious, Vanity Fair, The New Yorker, Wired, and many more. Passion is the core of our philosophy at Condé Nast. Our mission is not only to inform readers but to ignite and nourish their passions.
We’re seeking Machine Learning Engineers (MLE) to support initiatives such as recommendation systems, content understanding models, and user classification frameworks from within the Data Science group.
- Participate in model design, optimization, testing, quality assurance, and defect resolution
- Design and implement scalable systems to convert large volumes of data into useful model features, reports, and datasets
- Deliver and orchestrate machine learning infrastructure within production environments
- Collaborate with Engineers and Scientists to architect and implement a shared vision
- Participate in the entire software development lifecycle, from concept to release
- Applicants should have a degree (B.S. or higher) in a Computer Science or a related discipline, or relevant professional experience
- 2+ years of software development experience designing scalable systems related to machine learning, or more general statistical analysis
- Strong software development skills with proficiency in Python or C++
- Experience with machine learning frameworks such as TensorFlow, JAX, PyTorch, Spark MLlib, Keras, or scikit-learn
- Experience on cloud-based infrastructures such as AWS or GCP
- Exposure to orchestration platforms such as Apache Airflow or Kubeflow
- Proven attention to detail, critical thinking, and the ability to work independently within a cross-functional team
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