Senior Engineer, Machine Learning

About Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Engineer, Machine Learning – Remote in United States. This role offers the opportunity to design, build, and maintain scalable machine learning systems in a fast-paced, technology-driven environment.

Job Summary

You will work closely with cross-functional teams, including data scientists, software engineers, and business stakeholders, to deploy models into production and optimize performance across operations. The position focuses on ensuring model reliability, operational efficiency, and continuous improvement of ML workflows.

Key Responsibilities

• Deploy and monitor machine learning models in production using tools like Docker, Kubernetes, and MLflow to ensure scalability and reliability.
• Build and maintain data pipelines using Airflow, Spark, or Kafka to support model training and inference.
• Integrate ML models into business applications, collaborating with software engineers to operationalize solutions.
• Monitor model performance and detect data drift, implementing alerting and retraining pipelines.
• Clean, preprocess, and ensure high-quality data for machine learning applications.
• Collaborate with cross-functional teams to translate business problems into technical solutions.
• Maintain technical documentation for reproducibility and knowledge sharing.
• Optimize ML workflows to improve performance, scalability, and efficiency.

Requirements

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, Economics, Physics, or a related field, or equivalent experience.
• 3–5 years of experience in AI/ML engineering, data science, or software engineering with a machine learning focus.
• Strong understanding of the machine learning lifecycle, including training, deployment, and monitoring.
• Advanced programming skills in Python and experience with ML libraries such as Scikit-learn, TensorFlow, or PyTorch.
• Proficiency with MLOps tools including Docker, Kubernetes, MLflow, and CI/CD pipelines.
• Experience with data engineering tools and pipelines such as Airflow, Spark, and Kafka.
• Familiarity with cloud platforms like AWS, GCP, or Azure.
• Strong collaboration and communication skills to work with technical and non-technical stakeholders.
• Ability to work remotely aligned with Eastern Time Zone hours.

Benefits

• Competitive salary with performance-based incentives.
• Comprehensive health insurance (medical, dental, and vision).
• Retirement savings plan (401k) with employer contributions.