Remote ML Engineer: Train, Deploy & Improve AI Models

Spotter Labs Inc; Lemont (IL); Remote; USD 90,000 - 150,000; Full time

Benefits offered by this job

Fully remote work

Flexible working environment

Real-world AI projects

Job summary

Spotter Labs Inc. is seeking a Machine Learning Engineer to build and improve AI-powered products. You will work with large datasets, train and evaluate models, and collaborate with software engineers to deploy solutions.

This fully remote role offers a flexible environment and opportunities to tackle real-world AI challenges, stay current with ML advances, and contribute to production-ready systems.

Qualifications

• Basic understanding of machine learning concepts and algorithms.
• Experience with Python.
• Familiarity with ML libraries such as PyTorch, TensorFlow, or scikit-learn.
• Comfortable working with data and writing clean, maintainable code.
• Familiarity with Git.
• Good problem-solving skills.
• Strong written and verbal English communication.
• 1+ year of experience in machine learning, software engineering, data science, or a related technical role or strong personal projects.

Responsibilities

• Develop, train, and evaluate machine learning models.
• Prepare and analyze datasets for model training.
• Improve model performance through experimentation and testing.
• Deploy and maintain ML models in production environments.
• Collaborate with software engineers to integrate ML solutions into products.
• Monitor model performance and continuously improve accuracy.
• Stay up to date with new machine learning techniques and technologies.

Skills

Python

ML concepts

Problem-solving

Communication

Education

Bachelor's degree or related field

Tools

PyTorch

TensorFlow

scikit-learn

Git

Job description

Spotter Labs Inc. is seeking a Machine Learning Engineer to build and improve AI-powered products. You will work with large datasets, train and evaluate models, and collaborate with software engineers to deploy solutions.

This fully remote role offers a flexible environment and opportunities to tackle real-world AI challenges, stay current with ML advances, and contribute to production-ready systems.