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# Machine Learning Engineer

- **Company:** Be Applied Ltd.
- **Location:** United Kingdom (Remote available)
- **Salary:** £65,358
- **Contract:** Permanent contract
- **Skills:** AI Evaluation, Artificial Intelligence, Data Analysis, Automation of Tests, Digital Technology, Machine Learning, Tensorflow, Azure Machine Learning, Software Engineering, Application Enhancement Tool, Model Validation
- **Published:** November 7, 2026
- **Apply:** https://app.beapplied.com/apply/cpjsdq5avc

## About the Role

You'll have hands-on experience developing and evaluating machine learning or AI models, and a clear sense of how to pick the right approach for a problem, not just the newest one. This field moves quickly, so we don't expect years of experience with every technique. If you've spent a few months building and evaluating models, or you're coming from data science or software engineering and want to go deeper into machine learning, we'd love to hear from you., * Experience in leading the continuous improvement of machine learning models or AI systems, from development into production.

 * Knowledge of machine learning frameworks and cloud-based machine learning tools.
 * Experience in designing and running evaluation of model performance, including assurance of AI systems.
 * Knowledge of responsible AI principles, including fairness, bias detection and privacy-preserving data practices.
 * Desire to build beautiful, easy-to-use digital products with an understanding of the importance of accessibility in all its forms.
 * Strong communication skills and a collaborative approach to working across teams.

The successful candidate will have a desire to contribute across all areas to ensure Oak's success. You will be comfortable moving quickly and with agility, and be confident working with a range of digital systems (including proprietary ones, as required). You will continually look for ways that the team can keep getting better. You will be excellent at working as part of a remote team, building relationships and managing your time effectively

## Description

We're looking for a Machine Learning Engineer to help us build beautifully simple, high-impact digital products that support teachers and improve outcomes for pupils across the UK.

Oak is a fully remote, mission-driven organisation offering high levels of flexibility, autonomy, and purpose. We're a national not-for-profit working in partnership with teachers to create the highest-quality, sequenced curriculum and lesson resources for pupils across all subjects and age groups.

Our culture has been independently recognised through:

 * Flexa verified (93% overall score, including 95% for working hours and 97% for role modelling)
 * Escape the City's Top 1% Employers - based on anonymous colleague reviews of culture, development, and impact
 * Investors in People Gold - through external accreditation and colleague feedback

About the Role

You'll join our Intelligence Platform team, which uses our curriculum and data expertise to improve our products and keep our AI high-quality and safe. As our Machine Learning Engineer, you'll focus on the methodology behind the models that power our products and our shared evaluation service. That means turning product and user needs into well-posed problems, choosing and justifying the right data and techniques, and designing the evaluation that tells us whether a model is working.

This is a new role. We're splitting the traditional machine learning engineer role across two teams. You'll lead the develop, train and evaluate side, working closely with our Engineering Platform team's Machine Learning Platform Engineer, who owns the infrastructure that deploys and runs models. You'll also work with our Software Engineers, who build the application code that brings models into our products.

Teachers rely on our AI-powered tools, so evaluation, safety and responsible AI sit at the heart of this role. You'll work with EdTech partners and the DfE on AI quality and safety. You'll also help turn our curriculum and data expertise into reusable models and insight services that others can build on.

As part of the Oak team, you'll contribute to the wider success and culture of the organisation, and support and role model our five values: create the right environment, be a great colleague, own your role but work for the team, make things happen, and keep getting better

What you'll be doing:

 * Lead the continuous improvement of Oak's machine learning models and AI systems, working with Machine Learning Platform Engineers and Software Engineers to take them into production.
 * Coordinate the scoping of machine learning and AI problems, translating product and user needs into the right data and techniques.
 * Choose and justify appropriate statistical, machine learning and AI techniques for different problems, and keep evaluating new tools and methods.
 * Design the evaluation that assesses and improves our models, including automated testing and assurance of AI systems.
 * Contribute to Oak's shared AI evaluation, quality and safety services, including moderation and threat detection.
 * Help turn Oak's curriculum and data expertise into reusable models and insight services that internal teams and the wider ecosystem can build on.
 * Engage with EdTech partners and the DfE on AI evaluation, quality and safety.
 * Share knowledge and provide guidance to less experienced colleagues.
 * Work in cross-functional and product-oriented squads with colleagues from across the organisation, as required.
 * Deputise for other members of the Intelligence Platform team and take on other general responsibilities as required., Location: Remote, but you must be based in the UK with the legal right to work here
 * Sponsorship: Unfortunately, we're unable to offer visa sponsorship at this time
 * Closing date: Friday 20 November 2026 (We'll be reviewing applications as they come in and may close the role early)

If this sounds like the kind of role and team where you could do your life's best work, we'd love to hear from you.

You'll answer a few admin questions and some questions related to your day-to-day job. Your answers will go through our sift process: all answers will be anonymised, randomised, and then reviewed by a panel of reviewers (real humans), so it's your thinking that gets assessed, not your CV.

If you're shortlisted, we'll invite you to interview. We give everyone feedback at the end of the process.

Interview dates:

First stage interviews: Thursday 26 and Monday 30 November 2026

Technical Assessment interviews: Week commencing 7 December 2026