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# Junior Machine Learning Engineer (Python & PyTorch)

- **Company:** Hays Specialist Recruitment LLC
- **Location:** Atlanta, GA, United States (Remote available)
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Python (Programming Language), Machine Learning, NumPy, Data Processing, Pytorch, Pandas, Unsupervised Learning
- **Published:** November 7, 2026
- **Apply:** https://www.hays.com/en-US/job-detail/junior-machine-learning-engineer-%28python-pytorch%29-atlanta_1187244

## About the Role

Applicants must be legally authorized to work in the United States. Sponsorship not available.
Our client is seeking a Junior Machine Learning Engineer (Python & PyTorch) in Remote, GA., * 1-3 years of experience in machine learning, AI, data science, or a related technical field.

 * Strong proficiency in Python.
 * Hands-on experience building or training models with PyTorch.
 * Understanding of machine learning fundamentals, including supervised and unsupervised learning techniques.
 * Experience working with data manipulation libraries such as Pandas and NumPy.
 * Strong analytical and problem-solving skills.

## Description

Our client is seeking a Junior Machine Learning Engineer to support the development and deployment of machine learning models that advance the company's AI-driven initiatives. This is an excellent opportunity for an early-career engineer with strong Python skills and hands-on experience with PyTorch to work alongside experienced data scientists and engineers in a fast-paced startup environment.

 * Main duties will include: Write up and publish work in the form of company blogs and peer-reviewed articles'
 * Assist in the development, training, testing, and optimization of machine learning models.
 * Write clean, scalable code using Python and PyTorch.
 * Prepare, process, and validate large datasets for model training and evaluation.
 * Collaborate with data scientists and software engineers to implement AI solutions in production environments.
 * Monitor model performance and help troubleshoot issues related to data quality and model accuracy.
 * Document experiments, findings, and technical processes.