Machine Learning Engineer Job Description: Role Overview and Duties

The Machine Learning Engineer will design, build and operationalise machine learning models that solve real-world problems. Applicants should have practical experience across the full model lifecycle, strong statistical understanding and the ability to collaborate with product and engineering teams to deliver reliable, production-ready solutions.

Machine Learning Engineer Job Profile

This role is responsible for translating business requirements into robust machine learning solutions and ensuring those solutions perform reliably in production. The Machine Learning Engineer will work closely with data engineers, software engineers and product managers to integrate models into applications and maintain model performance over time.

The role requires a balance of applied research and engineering discipline, including experimentation, model evaluation and productionalisation. The successful candidate will focus on reproducibility, scalability and operational monitoring while adhering to software development best practice.

Machine Learning Engineer Job Description

The Machine Learning Engineer will lead the development and deployment of predictive models, from data preparation through to monitoring and iteration. Responsibilities include designing experiments, selecting appropriate modelling approaches, validating results and documenting findings to support decision making. Work will often involve handling large, complex datasets and applying statistical methods to extract actionable insights.

In the operational environment, the role requires ensuring models are robust, efficient and maintainable. Engineers will be expected to implement testing, version control and deployment practices that support reliability and reproducibility, and to collaborate with cross-functional teams to align model behaviour with product requirements and regulatory constraints.

Machine Learning Engineer: Duties and Responsibilities

• Translate business use cases into machine learning tasks and measurable success criteria.
• Design, prototype and iterate on predictive models and algorithms using sound statistical methods.
• Prepare and curate datasets for training, validation and testing, ensuring data quality and integrity.
• Perform feature engineering and selection to improve model performance and generalisation.
• Evaluate models using appropriate metrics and perform error analysis to identify improvement areas.
• Implement unit tests, model validation checks and reproducible pipelines for experiments.
• Collaborate with software and data engineering teams to integrate models into production systems.
• Develop and maintain deployment processes and versioning for models and data artefacts.
• Monitor model performance in production and implement retraining or recalibration strategies as needed.
• Optimise models for latency, throughput and resource efficiency while maintaining accuracy.
• Document modelling decisions, experiment results and operational procedures clearly and concisely.
• Participate in code reviews and share knowledge to improve team practices and standards.
• Ensure compliance with data governance, privacy and security requirements in model development.
• Support cross-functional stakeholders by communicating technical concepts in accessible terms.

Machine Learning Engineer: Requirements and Qualifications

• Bachelor's or higher degree in computer science, statistics, mathematics, engineering or a related discipline.
• Proven experience applying machine learning algorithms in a production environment.
• Strong foundation in statistical modelling, probability and evaluation metrics.
• Practical experience with the full machine learning lifecycle from data ingestion to monitoring.
• Solid software engineering skills including code quality, testing and version control.
• Experience working with large and complex datasets and understanding of data preprocessing techniques.
• Ability to design experiments and interpret results with rigour and clarity.
• Experience operationalising models and implementing monitoring and retraining strategies.
• Good problem solving skills and attention to detail when diagnosing model issues.
• Effective communication skills for working with technical and non-technical stakeholders.
• Ability to work independently and as part of cross-functional teams in an agile environment.
• Commitment to documentation, reproducibility and continuous improvement of models and processes.