Applied Machine Learning (ML) Engineer, Machine Learning (ML) and Artificial Intelligence (AI) Required, Work From Home

About this role

This is a Fully Remote Job

About Our Client:

The organization operates within the artificial intelligence and machine learning industry, focusing on developing AI-native applications. It addresses challenges in classification, retrieval, ranking, personalization, and evaluation within machine learning, delivering practical solutions that impact real users through scalable and reliable systems.

About the Opportunity:

The APPLIED MACHINE LEARNING ENGINEER role is responsible for building and enhancing machine learning models and systems that power AI-driven features. This position contributes by developing and deploying models for classification, ranking, and personalization, ensuring model quality and system reliability to meet user needs. The role is fully remote.

Responsibilities:

• Build and improve machine learning models for classification, ranking, retrieval, and personalization
• Optimize classifiers for accuracy, precision, latency, and cost
• Enhance search relevance using embeddings, hybrid search, and reranking
• Develop personalization models respecting user privacy
• Create evaluation pipelines, labeled datasets, and metrics to assess model quality
• Fine-tune and adapt models based on available data
• Maintain training, inference, and deployment pipelines
• Debug production issues and monitor model performance
• Collaborate with engineering and product teams to deliver reliable ML-powered features

Requirements:

• Experience in machine learning
• Experience in artificial intelligence
• Strong foundation in machine learning and modern neural architectures
• Hands-on experience deploying machine learning models in production
• Background in classification, ranking, retrieval, recommendation systems, or personalization
• Understanding of model evaluation, experimentation, and performance metrics
• Ability to write production-quality code and work with real-world datasets
• Skill in balancing model quality, latency, cost, and reli...