Machine Learning/ML Engineer

Role Overview

Design, develop, and deploy machine learning systems that operate at scale within production-grade environments. Contribute to building end-to-end ML pipelines and work closely with distributed data systems and modern ML frameworks.

What You Will Do

Design and build machine learning models and pipelines, develop scalable data processing workflows, collaborate with cross-functional teams, implement and maintain ML infrastructure components, and optimize models for performance and reliability.

Why It Might Be a Fit

This is a highly technical role suited for engineers who enjoy solving complex data-driven problems and working across infrastructure, modeling, and deployment layers. The environment is fast-paced and collaborative, with strong emphasis on engineering excellence and real-world impact.

Requirements

• 5+ years of industry experience in Machine Learning engineering, or PhD with 2+ years of applied ML experience
• Strong programming skills in Python, Java, Scala, or similar languages
• Hands-on experience with distributed data processing frameworks such as Spark, Hive, and Airflow
• Experience with ML frameworks such as TensorFlow or PyTorch
• Solid understanding of production machine learning systems and engineering best practices
• Experience deploying and operationalizing ML models in real-world environments
• Familiarity with Docker and Kubernetes is highly desirable
• Experience building end-to-end ML infrastructure is a strong advantage
• Strong problem-solving skills with the ability to work on complex, large-scale systems
• Ability to work independently in a remote, fast-paced contract environment

Benefits

• 100% remote contract opportunity within the United States
• Competitive daily W2 compensation
• Opportunity to work on large-scale machine learning systems in production environments
• Exposure to modern ML frameworks and distributed data processing technologies
• Hands-on experience building and operationalizing end-to-end ML infrastructure
• Collaborative engineering environment focused on scalability and performance
• Opportunity to contribute to impactful, real-world machine learning applications