Machine Learning Engineer

About the Role

Our client is seeking an innovative Machine Learning Engineer to join their advanced research and development team in Cambridge . This role is at the forefront of developing and deploying machine learning models that solve complex business problems and drive product innovation. You will work with large datasets, build sophisticated algorithms, and collaborate with data scientists and software engineers to bring ML solutions into production. We are looking for a candidate with a strong theoretical foundation in ML and practical experience in building and scaling ML systems.

Key Responsibilities

• Design, build, train, and deploy machine learning models for various applications.
• Develop and maintain ML pipelines for data processing, feature engineering, and model evaluation.
• Collaborate with data scientists to transition research models into production-ready code.
• Optimize ML models for performance, scalability, and efficiency in production environments.
• Stay current with the latest research and advancements in machine learning and artificial intelligence.
• Work with large datasets and implement data preprocessing techniques.

Requirements

• Master's or Ph.D. in Computer Science, Machine Learning, Statistics, or a related quantitative field.
• Proven experience (4+ years) as a Machine Learning Engineer or in a similar role.
• Proficiency in programming languages such as Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong understanding of machine learning algorithms, statistical modeling, and data mining techniques.
• Experience with cloud platforms (AWS, Azure, GCP) and ML services.
• Familiarity with big data technologies and distributed computing frameworks is a plus.

Benefits

• Highly competitive salary and bonus potential.
• Comprehensive health, dental, and vision insurance.
• Generous paid time off and flexible holidays.
• Significant investment in professional development, including conferences and training.
• Access to cutting-edge technology and research opportunities.
• Hybrid working model to foster collaboration and work-life balance.