Machine Learning Engineer - Remote

Role Overview

You will join Field AI as a Machine Learning Engineer (MLOps Engineer) to build and maintain the infrastructure that deploys, monitors, and optimizes machine learning models in real-world robotics environments.

Responsibilities

• Design and develop scalable ML infrastructure to support data generated by a global fleet of robots.
• Manage and optimize large-scale data pipelines for continuous streams from deployed robots.
• Implement model versioning, reproducibility, and efficient retraining workflows.
• Use cloud infrastructure (AWS, Azure, GCP) for training, deployment, and monitoring at scale.
• Deploy ML models into production and ensure smooth integration and performance.
• Automate monitoring and maintenance to detect model drift and trigger retraining.
• Troubleshoot issues across deployment, performance, and system integration.
• Optimize system architecture for scalability, reliability, and performance of ML deployments.
• Build and improve CI/CD pipelines for continuous delivery of reliable models.

Requirements

• 3+ years of experience in MLOps, DevOps, or similar roles in robotics or data-intensive environments.
• Strong understanding of ML frameworks: TensorFlow, PyTorch, Scikit-learn.
• Hands-on experience with Docker and Kubernetes.
• Proficiency with cloud platforms: AWS, Azure, GCP.
• Experience building CI/CD pipelines for ML workflows.
• Familiarity with Git.
• Strong system architecture, deployment strategy, and troubleshooting/problem-solving skills.

Nice-to-Haves

• Not explicitly stated.

Benefits / Compensation

• Competitive salary $70,000–$300,000 annually (commensurate with experience and location).
• Flexible work arrangements, including hybrid and remote options.
• Health, dental, and vision insurance; professional development opportunities.