Machine Learning Engineer for Educational Assessment

At AQA, we’re committed to advancing education and we’re committed to our people. As the largest provider of academic qualifications in the UK, we mark over 10 million exam papers each year and it’s our people who make this happen.Machine Learning Engineer (Education Research)  Permanent Manchester: £34,000 - £36,900 /  Milton Keynes: £35,400 - £38,400  Working Arrangements; Hybrid - two days per week in the office IntroductionAQA is building its AI for assessment capability and is looking for a junior machine learning engineer who wants to apply AI and machine learning to meaningful educational challenges.This is a distinctive early-career opportunity to work across the full applied machine learning lifecycle: researching and testing new approaches, evaluating them rigorously, and helping turn successful prototypes into reliable capabilities that can be deployed within assessment products and services.You will join AQA’s in-house AI for assessment lab and work alongside experienced AI researchers, software developers, product teams, psychometricians and assessment experts. You will receive support to develop both your research and engineering skills while contributing to work with real educational purpose.Purpose of the roleYou will contribute to the research, development and productionisation of AI capabilities for educational assessment. These may include automated marking, feedback generation, learner support, skill estimation, proficiency modelling and adaptive testing.The role combines applied research with practical engineering. You will help investigate and validate promising approaches, then work collaboratively with technical and product colleagues to turn successful research into reproducible, maintainable and deployable machine learning capabilities.Key responsibilities·  Design, develop and refine machine learning models and prototypes that support educational assessment, helping to translate assessment needs into practical AI solutions and providing evidence for future development decisions.·  Evaluate model performance against technical and assessment measures, including accuracy, fairness, bias, reliability and alignment with human marking standards, while ensuring methods and results are clearly documented and reproducible.·  Work collaboratively with AI researchers, developers, psychometricians and product teams to build, deploy and continuously improve machine learning solutions, developing robust engineering practices and end-to-end experience across the full AI lifecycle.What we are looking forEssentialStrong Python skills, with practical experience using relevant data and machine learning libraries such as NumPy, Pandas and scikit-learn.Practical experience with at least one deep-learning framework, such as PyTorch or TensorFlow.A good foundation in machine learning, including supervised learning and model evaluation.A good understanding of the machine ...