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# Machine Learning Engineer

- **Company:** The Ladders
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $155,520.0 - $228,700.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Cloud Computing, Python (Programming Language), Machine Learning, Software Engineering, SQL Databases, Containerization, Kubernetes, Machine Learning Operations, Data Pipelines, Docker, Service Stack
- **Published:** September 5, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pho54rg76k

## About the Role

* 5+ years of experience in building and operating ML systems in production environments
 * Strong foundation in ML/AI concepts such as statistics and optimization applied to real-world challenges
 * Proficient in Python, Java, and SQL, with solid software engineering principles
 * Hands-on experience with data pipelines and cloud platforms, including orchestration tools like Airflow or Kubeflow
 * Familiarity with MLOps tooling and machine learning lifecycle best practices
 * Knowledge of containerization and cloud infrastructure, such as Docker and Kubernetes
 * Excellent communication skills for conveying complex technical concepts clearly

## Description

This role will focus on building and operating production-grade machine learning systems that improve reliability, scalability, and business impact across the technology stack. You will partner closely with cross-functional stakeholders to translate ambiguous problem statements into practical ML solutions and measurable outcomes. The position also requires strong collaboration with engineering and operations teams to deliver compliant services, strengthen monitoring, and support continuous improvement across the model lifecycle., * Partner with stakeholders to analyze and clarify ML problem statements

 * Design and maintain scalable ML solutions in a production environment
 * Create reproducible ML workflows with modern orchestration tools
 * Implement frameworks to monitor and evaluate model performance and data quality
 * Collaborate on delivering compliant ML-powered services across disciplines
 * Demonstrate system design rationale and understanding of end-to-end models
 * Drive operational excellence, including incident response and customer feedback handling


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