Machine Learning Engineer (Recommender Systems)

TL;DR

Machine Learning Engineer (Recommender Systems): Designing and implementing advanced recommender systems for large-scale data platforms with an accent on model architecture and production deployment. Focus on building scalable pipelines using Spark and Databricks, optimizing deep learning models like Transformers and Two-Tower architectures, and ensuring high-performance model integration for personalized user experiences.

Location: Must be based in Latin America

Company

hirify.global helps U.S. companies build and scale AI and data teams by connecting them with top-tier talent in Latin America.

What you will do

• Design and implement recommender systems to enhance product discovery and engagement.
• Build scalable machine learning pipelines for data processing, feature engineering, and model deployment.
• Optimize advanced recommendation models including Wide & Deep, Two-Tower, Transformer-based, and deep sequential models.
• Collaborate with engineers and stakeholders to integrate models into production environments.
• Monitor and continuously enhance model performance, reliability, and accuracy.
• Stay current with advancements in ML, deep learning, and Generative AI to drive product innovation.

Requirements

• 5+ years of experience as a Machine Learning Engineer.
• Must have: Minimum 1 year of hands-on experience designing and building recommender systems.
• Strong proficiency in Python and ML frameworks like TensorFlow, PyTorch, or scikit-learn.
• Extensive experience with large-scale data processing using Spark and Databricks.
• Solid understanding of machine learning algorithms, statistical modeling, and A/B testing methodologies.
• Must be based in Latin America.
• Excellent verbal and written communication skills in English.

Culture & Benefits

• Ownership through equity participation.
• Annual company retreat and optional in-person meetups.
• Education bonus for continuous learning and professional development.
• Paid time off and company-wide winter break.
• High-performance, collaborative, and transparent work culture.
• Tailored career roadmaps to support your professional growth.