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Junior Machine Learning Research Engineer - Job Opportunity at Cambridge University Press & Assessment

Cambridge, United Kingdom
Full-time
Entry-level
Posted: July 10, 2025
Hybrid
GBP 40,000 - 52,000 per year

Benefits

Comprehensive 28 days annual leave plus bank holidays providing superior work-life balance compared to standard UK offerings
Private medical insurance and Permanent Health Insurance ensuring comprehensive healthcare coverage beyond NHS services
Performance-based discretionary annual bonus system rewarding exceptional contributions
Group personal pension scheme with employer contributions supporting long-term financial security
Life assurance coverage up to 4x annual salary providing substantial family protection
Green travel schemes promoting environmental sustainability and cost savings
Flexible and hybrid working arrangements from day one supporting modern work preferences
Disability accommodation and adjustment support demonstrating inclusive workplace commitment

Key Responsibilities

Drive innovation in AI-powered learning solutions by developing, deploying, and maintaining scalable machine learning systems that directly impact Cambridge English product success and market leadership
Optimize and modularize AI models for maximum reusability and performance, establishing technical standards that influence the organization's AI infrastructure strategy
Lead cross-functional collaboration with product, validation, and business teams to transform cutting-edge AI capabilities into market-ready features that generate measurable business value
Position Cambridge as an industry leader by staying at the forefront of AI and machine learning trends, continuously evaluating and implementing emerging technologies
Manage strategic task planning and prioritization aligned with business objectives, demonstrating autonomy in driving technical initiatives that support organizational goals

Requirements

Education

First class Bachelor's or Master's degree (or equivalent qualifications and experience) in machine learning or AI/computer science with a substantial machine learning component

Experience

Junior level with demonstrable experience in developing, training, and deploying models

Required Skills

Excellent programming skills in languages such as Python and shell scripting with a strong focus on code optimization, modular design, and efficiency Demonstrable experience in developing, training, and deploying models using frameworks like PyTorch and TensorFlow Strong understanding of data analysis, model evaluation, and error analysis to drive continuous model improvement Excellent communication skills Demonstrable commitment to continuous learning, staying current with state-of-the-art research and applying emerging AI techniques Proven ability to collaborate effectively in Agile/Scrum teams and contribute to cross-functional projects Experience applying machine learning to educational assessment and learning solutions Hands-on experience with Large Language Models (LLMs) or foundation models, including fine-tuning and adapting models for specific, production-level applications
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Sauge AI Market Intelligence

Industry Trends

Educational technology is experiencing unprecedented growth driven by AI integration, with personalized learning platforms becoming the dominant market force as institutions seek to scale individualized instruction. The sector is moving beyond traditional assessment methods toward adaptive, AI-driven evaluation systems that provide real-time feedback and learning analytics. Large Language Models and foundation models are revolutionizing educational assessment, enabling sophisticated natural language processing capabilities for automated essay scoring, conversational tutoring systems, and multilingual learning support. This technological shift is creating new opportunities for organizations that can effectively implement and scale these technologies. The hybrid work model has become permanently established in the tech industry, with organizations offering flexible arrangements to attract top talent in an increasingly competitive market. This shift is particularly pronounced in AI and machine learning roles where remote collaboration tools have proven highly effective for research and development activities.

Salary Evaluation

The offered salary range of £40,000-£52,000 is competitive for a junior ML engineer position in Cambridge, sitting at the upper end of the typical £35,000-£50,000 range for similar roles in the UK. The proximity to Cambridge University and the organization's academic prestige justify this premium, while the comprehensive benefits package adds significant value beyond base compensation.

Role Significance

The role is part of a growing Applied AI team with multiple concurrent hiring initiatives, suggesting a team expansion from approximately 5-8 members to 10-15 members. This growth phase indicates increased investment in AI capabilities and provides opportunities for rapid professional development within a scaling organization.
This junior-level position serves as a strategic entry point into AI research and development within a prestigious academic organization, offering direct exposure to cutting-edge research while contributing to products that impact global education. The role provides significant autonomy for a junior position, with responsibilities that include strategic planning and cross-functional leadership.

Key Projects

Development of AI-powered assessment tools that can evaluate complex language skills across multiple proficiency levels and cultural contexts Implementation of personalized learning pathways using machine learning algorithms to adapt to individual student needs and learning styles Creation of automated scoring systems for Cambridge English qualifications that maintain the rigor and reliability expected from a world-leading assessment organization Research and development of multilingual AI models that can support Cambridge's global reach in language education and assessment

Success Factors

Technical excellence in machine learning implementation combined with deep understanding of educational psychology and assessment theory, enabling the development of AI solutions that are both technically sophisticated and pedagogically sound Strong collaborative skills that enable effective partnership with diverse stakeholders including educators, product managers, and business leaders, translating between technical capabilities and educational needs Continuous learning mindset that keeps pace with rapidly evolving AI research while maintaining focus on practical application within educational contexts Systems thinking approach that considers the scalability, maintainability, and ethical implications of AI solutions deployed in educational settings Cultural sensitivity and global perspective that enables development of AI systems suitable for diverse international markets and educational contexts

Market Demand

High demand driven by the convergence of AI advancement and educational technology growth, with particular strength in roles combining machine learning expertise with domain-specific knowledge in assessment and learning sciences.

Important Skills

Critical Skills

Python programming proficiency is essential as the primary language for machine learning development, requiring not just basic competency but expertise in optimization, modular design, and production-ready code development that can scale across global educational platforms Deep learning frameworks expertise in PyTorch and TensorFlow is crucial for developing and deploying sophisticated AI models, particularly given the organization's focus on natural language processing and adaptive assessment systems Data analysis and model evaluation skills are fundamental for ensuring AI systems meet the rigorous standards expected in educational assessment, where accuracy, fairness, and reliability are paramount Communication skills are critical for translating complex technical concepts to educational stakeholders and collaborating effectively across multidisciplinary teams including educators, linguists, and product managers

Beneficial Skills

Large Language Model experience provides significant advantage given the rapid adoption of LLMs in educational technology and the organization's need to stay competitive in AI-powered language assessment Educational technology domain knowledge offers substantial value for understanding the unique requirements and constraints of learning and assessment systems Research experience and publication record demonstrate the analytical thinking and innovation capabilities valued in this research-focused environment Agile development methodology experience supports effective collaboration in the organization's modern development practices and cross-functional project approach

Unique Aspects

Opportunity to work on AI systems that directly impact global education, with Cambridge English qualifications taken by millions of learners worldwide, providing unprecedented scale and reach for machine learning applications
Access to vast datasets of language learning and assessment data spanning multiple languages, proficiency levels, and cultural contexts, offering rich opportunities for AI research and development
Integration with University of Cambridge research community providing exposure to cutting-edge academic research and potential collaboration opportunities with world-renowned researchers
Focus on responsible AI development within educational contexts, addressing critical issues of fairness, bias, and accessibility in AI-powered assessment systems
Hybrid work model with only 2 days per week in office, unusual for research-intensive roles and providing exceptional work-life balance in the competitive Cambridge market

Career Growth

Progression to mid-level engineer typically occurs within 18-24 months given the organization's growth trajectory and the accelerating demand for AI expertise in educational technology. Advanced roles become accessible within 3-5 years with demonstrated impact and continued skill development.

Potential Next Roles

Machine Learning Research Engineer (mid-level) with increased technical leadership responsibilities and project ownership Senior Machine Learning Research Engineer focusing on advanced research initiatives and technical mentorship AI Product Manager combining technical expertise with business strategy and product development Research Scientist in educational AI with opportunities to publish research and represent the organization at international conferences Technical Lead for AI initiatives with responsibility for team management and strategic technology decisions

Company Overview

Cambridge University Press & Assessment

Cambridge University Press & Assessment represents a unique combination of academic prestige and commercial innovation, serving as both a leading academic publisher and a global assessment organization. As part of the University of Cambridge, the organization benefits from access to world-class research while maintaining the operational efficiency and market responsiveness of a commercial enterprise.

The organization holds a dominant position in English language assessment globally, with Cambridge English qualifications recognized by thousands of institutions worldwide. This market leadership provides stable revenue streams that support investment in innovative AI technologies and research initiatives.
Cambridge serves as the global headquarters and primary research hub, benefiting from the city's concentration of academic and technology talent. The location provides access to University of Cambridge research collaborations and the broader Cambridge technology ecosystem, creating opportunities for knowledge exchange and talent acquisition.
The organization combines academic rigor with commercial pragmatism, fostering an environment that values both theoretical excellence and practical application. The culture emphasizes continuous learning, research collaboration, and global impact, while maintaining the collegial atmosphere typical of academic institutions.
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