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# Machine Learning Engineer

- **Company:** HRB Digital LLC.
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Salary:** $91,000.0 - $145,600.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Code Review, Data Sharing, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Unstructured Data, Pytorch, Deep Learning, Generative AI, Scikit Learn, Information Technology, Machine Learning Operations, Spacy
- **Published:** July 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5054da40d563c570

## About the Role

* Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field (or equivalent experience).
 * 3 years total experience.
 * Hands-on experience with ML frameworks such as scikit-learn, TensorFlow, PyTorch, and spaCy.
 * Proficiency in Python and familiarity with ML development workflows.
 * Strong analytical and problem-solving skills with attention to detail.
 * Effective communication and collaboration skills in a team-oriented environment.
 * Experience with generative AI is a plus.

Preferred Qualifications:

 * Master's or PhD in Computer Science, Statistics, Mathematics, or a related field (or equivalent experience).
 * Hands-on experience with building and deploying Machine Learning models for real time inference.

## Description

The Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models to solve real-world business challenges. This role focuses on hands-on model development, data preparation, and integration of ML solutions into production systems. The ideal candidate is a strong collaborator who works closely with fellow Machine Learning Engineers, MLOps Engineers, and cross-functional teams to deliver scalable and impactful AI solutions, including generative AI models., * Model Development: Design, build, and deploy machine learning models-including generative AI models-to address complex business problems.

 * Data Collaboration: Work with teammates to preprocess, clean, and analyze structured and unstructured data for use in ML pipelines.
 * Performance Optimization: Tune and optimize models for accuracy, efficiency, and scalability in production environments.
 * Integration Support: Collaborate with MLOps Engineers to ensure smooth deployment, monitoring, and maintenance of ML models.
 * Continuous Learning: Stay informed on the latest advancements in machine learning, deep learning, and generative AI technologies, and apply them where appropriate.
 * Team Collaboration: Contribute to a collaborative engineering culture by sharing knowledge, participating in code reviews, and supporting team initiatives.


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