5+ years of experience in machine learning engineering or related roles
Strong foundation in computer science and applied mathematics
Proficiency in Python and ML frameworks like TensorFlow, PyTorch, or Scikit-learn
Experience with cloud-based ML services (e.g., SageMaker, Vertex AI, Azure ML)
Familiarity with MLOps practices including CI/CD for ML and model versioning
Strong understanding of data structures, algorithms, and software engineering principles
Knowledge of ethical AI principles and model interpretability
Responsibilities
Design, develop, and deploy scalable machine learning models to solve real-world problems
Optimize model performance through hyperparameter tuning and feature engineering
Build and maintain robust data pipelines and infrastructure for model training
Implement monitoring and logging solutions to track model performance
Collaborate with cross-functional teams to integrate ML solutions into applications
Ensure models adhere to security, privacy, and compliance standards
Maintain detailed documentation for model architecture and deployment workflows
Benefits
Opportunity to work on diverse projects across various domains
Access to the latest advancements in machine learning and AI frameworks
Collaborative environment with data scientists and software engineers
Focus on ethical AI principles and model interpretability
Potential for continuous learning and professional development
Full Job Description
Project Details
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.
Job Experience
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.
Hourly Rate: 42-55
About Oxford Global Resources
Oxford Global Resources is a staffing and recruiting company that provides temporary and permanent staffing solutions to clients in a variety of industries. The company was founded in 1984 and is headquartered in Cambridge, Massachusetts. Oxford Global Resources offers a range of services, including contract staffing, direct hire, and temp-to-perm staffing. The company's clients include some of the world's leading companies in the biotechnology, pharmaceutical, and medical device industries, as well as government agencies and other organizations. Oxford Global Resources is committed to providing high-quality staffing solutions that meet the unique needs of each client.
