AQA
Machine Learning Engineer for Educational Assessment
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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
Introduction
AQA 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 role
You 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 for
Essential
- Strong 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 learning lifecycle, from data ingestion and cleaning through to model development and validation.
- An interest in education or educational assessment and motivation to apply technology in support of AQA’s mission.
- Strong communication and collaboration skills, including the ability to explain technical ideas clearly and learn from colleagues across different disciplines.
Desirable
- NLP knowledge or experience relevant to text-based assessment.
- Familiarity with NLP libraries or frameworks such as Hugging Face Transformers or spaCy.
- Experience of, or exposure to, building end-to-end machine learning systems, including deployment.
- Familiarity with software-engineering practices such as version control, testing, code review and technical documentation.
- Exposure to sequential modelling.
- Experience working with multimodal data, including data processing and synchronisation.
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What’s in it for you
This is an opportunity to build an applied AI career in an environment combining research, engineering and real educational purpose.
Unlike many early-career research roles, you will have the opportunity to follow promising work beyond the prototype: learning how models are evaluated, engineered, integrated and deployed within real products and services. You will gain practical experience across the full machine learning lifecycle while working with experienced specialists in AI, software development, product development, psychometrics and educational assessment.
You will:
- Contribute to AI capabilities in areas such as automated marking, personalised feedback, item generation and learner support;
- Develop practical experience of both applied ML research and production engineering;
- Learn how responsible AI capabilities are tested, deployed, monitored and improved;
- See how your work contributes to products and services used to address meaningful educational needs.
A 35‑hour working week with flexible, hybrid working
25 days’ annual leave (rising to 30), plus Christmas closure days
Excellent pension (up to 11.5% employer contribution) etc
Diversity and Inclusion Statement
At AQA, we are committed to fostering a workplace that celebrates diversity and promotes equity and inclusion. We believe that a diverse team brings richer perspectives and drives better outcomes. Our ED&I strategy ensures that everyone—regardless of religion, ethnicity, gender identity or expression, age, disability, sexual orientation, or background—is valued, respected, and empowered to thrive. We actively promote inclusive language, avoid stereotypes, and strive for representation across all dimensions of diversity. We welcome applications from individuals of all backgrounds and lived experiences.
Application Process
To apply, please submit your CV through the AQA careers site. Applications close on Sunday 9 August 2026.
Please include a link to a machine learning project you can share with us, such as a GitHub or other accessible repository, that showcases relevant technical skills for this role.
Stage 1: a 30-minute Teams interview where you will talk through the shared project or artefact and discuss the technical decisions behind it.
Stage 2: a face-to-face interview in Manchester or Milton Keynes, focused on your wider professional experience, collaboration style and motivation for educational assessment.
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Recruitment Agencies
We have a preferred supplier list (PSL) in place.
Unsolicited CVs will be treated as a gift. We will not be subject to or liable under your terms and conditions for agency fees.
Full Job Description
Summary
Accountable to the Head of AI for Assessment Innovation, the overall purpose of this role is to develop models and algorithms as required by new assessment products and services.
The post holder will research and develop AI capabilities that can enable new assessment products, increase the breadth of assessment services on offer and help shape long-term tech innovation and solutions. They will ideate and develop proofs of concept and prototypes and ensure they are cutting-edge, relevant and fit-for-purpose.
Research, development and evaluation of AI solutions for assessment are key enablers in a range of diversification, digitisation and customer programmes. The AI for Assessment Innovation team is AQA’s in-house AI for assessment lab, providing services and solutions alongside and in collaboration with contractors and partners. The team’s responsibilities are:
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- Research and development of AI features for new products or as part of contracted services
- EdTech partnership support through targeted evaluations and testing of third-party AI tools
- Providing AI for assessment expertise to the whole AQA group and advancing AQA’s knowledge and know-how
The role sits within the AI for Assessment Innovations team, in the Assessment Research and Innovation business area. Reporting to the Head of AI for Assessment, the role collaborates with a team of AI researchers, developers and managers and will have line management responsibility for AI for Assessment apprentices.
Activities:
AI model development for assessment
- Design, build, and refine machine learning models that support educational assessment use cases, such as automated marking (e.g., essays, short answers), feedback generation and learner support, skill estimation, proficiency modelling, and adaptive testing.
- Select appropriate modelling approaches (e.g., NLP models, classical ML, or deep learning) based on pedagogical and product requirements.
- Conduct rigorous experimentation, including hyperparameter tuning and ablation studies, to improve model performance and fairness.
Educational assessment research
- Work with complex educational datasets (e.g., learner responses, interaction logs, assessment outcomes).
- Design evaluation frameworks that go beyond accuracy to include fairness and bias across learner groups, marking reliability and consistency, alignment with human marking standards and mark schemes.
- Work closely with psychometricians, assessment experts and product teams to translate educational requirements into technical solutions.
- Incorporate domain knowledge (e.g., marking schemes, assessment objectives, curriculum standards) into model design.
From prototype to operationalisation
- Develop scalable pipelines for data processing, model training, validation, and deployment.
- Collaborate and support the teams responsible for integrating models into production systems.
- Contribute to CI/CD workflows, model versioning, and reproducibility practices.
Responsible AI and governance
- Identify, assess, and mitigate risks related to bias, fairness, and misuse in assessment AI systems.
- Contribute to the development of explainable and transparent AI systems suitable for high-stakes exams or classroom use.
- Work with the relevant AQA teams to ensure compliance with relevant regulatory and ethical standards in education.
Documentation and knowledge sharing
- Document model architectures, decisions, evaluation results, and limitations.
- Communicate findings clearly to both technical and non-technical stakeholders.
- Contribute to internal best practices, reusable components, and knowledge sharing across teams.
To be successful in this role, you will need to demonstrate:
Essential
Motivation
- A keen interest in the education or educational assessment sector, and a drive to furthering AQA’s mission.
Machine learning and NLP expertise
- Understanding of machine learning techniques, including supervised learning, model evaluation and optimisation.
- Natural Language Processing (NLP) for text-based assessment and some knowledge of multi-modal models.
- Experience building end-to-end ML systems from data ingestion to deployment.
- Familiarity with model interpretability techniques (e.g., SHAP, LIME).
Engineering
- Proficient in Python and core ML/data libraries (e.g
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