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# Machine Learning Engineer

- **Company:** Oxford Global Resources
- **Location:** Irvine, CA, United States
- **Experience:** Expert
- **Salary:** $140,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Cloud Computing, Continuous Integration, Data Structures, Distributed Systems, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Azure Machine Learning, Software Engineering, Feature Engineering, Pytorch, Delivery Pipeline, Deep Learning, Containerization, Scikit Learn, Kubernetes, Information Technology, Deployment Automation, Machine Learning Operations, Software Version Control, Data Pipelines, Docker
- **Published:** September 24, 2026
- **Apply:** https://www.jofdav.com/jobs/59850951-machine-learning-engineer

## About the Role

* 5+ years' experience in machine learning engineering or related roles with a strong foundation in computer science and applied mathematics.
 * Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
 * Experience with cloud-based ML services (e.g., SageMaker, Vertex AI, Azure ML) and containerization tools (Docker, Kubernetes).
 * Strong understanding of data structures, algorithms, and software engineering principles.
 * Familiarity with MLOps practices including CI/CD for ML, model versioning, and automated deployment.
 * Experience working with large-scale datasets and distributed computing environments.
 * Excellent problem-solving skills and ability to translate business requirements into technical solutions.
 * Knowledge of ethical AI principles, model interpretability, and fairness in machine learning.

## Description

* Design, develop, and deploy scalable machine learning models and systems to solve real-world problems across various domains.
 * Optimize model performance through hyperparameter tuning, feature engineering, and algorithm selection.
 * Build and maintain robust data pipelines and infrastructure to support model training and inference.
 * Implement monitoring and logging solutions to track model performance, detect drift, and ensure reliability in production environments.
 * Collaborate with data scientists, software engineers, and product teams to integrate ML solutions into applications and services.
 * Ensure models adhere to security, privacy, and compliance standards throughout the development lifecycle.
 * Maintain detailed documentation for model architecture, training processes, and deployment workflows.
 * Stay up-to-date with the latest advancements in machine learning, deep learning, and AI frameworks. Continuously explore opportunities to improve model accuracy and efficiency.


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