Machine Learning/ML Engineer in United States at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning/ML Engineer in the United States.

This role focuses on designing, developing, and deploying machine learning systems that operate at scale within production-grade environments. You will contribute to building end-to-end ML pipelines, from data processing and feature engineering to model training, evaluation, and deployment. The position involves working closely with distributed data systems and modern ML frameworks to deliver reliable, high-performance solutions. You will help operationalize models in production environments, ensuring scalability, robustness, and maintainability. 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.

• Design, build, and deploy machine learning models and pipelines for production use cases.
• Develop scalable data processing workflows using distributed systems and frameworks.
• Collaborate with cross-functional teams to translate business requirements into machine learning solutions.
• Implement and maintain ML infrastructure components supporting training, inference, and monitoring.
• Optimize models for performance, scalability, and reliability in production environments.
• Work with big data tools and frameworks to process and prepare large-scale datasets.
• Ensure adherence to engineering best practices, including code quality, testing, and CI/CD workflows.
• Support deployment and orchestration of ML workflows using modern infrastructure tools.

• 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.

• 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.