Machine Learning Engineer

• Apply mathematical, statistical, and scientific reasoning to form hypotheses, quantify uncertainty, and interpret results
• Design and run structured experiments to assess model behaviour, performance, and user impact
• Work with real, imperfect operational data, not just curated or static datasets
• Collect, assess, and transform data to support model evaluation and continuous improvement
• Balance rigour with pragmatism, delivering solutions that are robust, proportionate, and fit for purpose
• Integrate machine learning components into wider systems, considering performance, reliability, and operational constraints
• Communicate complex technical ideas clearly to non-technical stakeholders, enabling informed decision-making
• Engage confidently in deep technical design and review discussions with peers
• Operate effectively within a strong technical assurance and review culture
• Collaborate with internal teams and selected external partners working at the leading edge of AI

This role suits a senior ML practitioner who values judgement, evidence, and outcomes over theoretical or tooling purity., * Proven experience operating at senior practitioner level as a Machine Learning Engineer, AI Engineer, Applied ML Scientist, or equivalent

• Strong grounding in applied mathematics, statistics, and scientific practice
• Demonstrated ability to evaluate ML models using quantitative evidence and structured experimentation
• Excellent Python skills for building, evaluating, and iterating on ML solutions
• Experience working with real-world, imperfect data from operational systems
• Strong software engineering practices, including readable, maintainable, and well-tested code
• Experience integrating ML components into broader production systems
• Clear understanding of data ethics, privacy, and responsible use of data
• Strong communication skills across technical and non-technical audiences
• Proven ability to lead work independently and take ownership of outcomes

Technologies you’ll encounter

The environment evolves, but typical tools include:

• Python for experimentation, modelling, and evaluation
• Weights & Biases (or equivalent) for experiment tracking
• AWS, including services such as SageMaker and Bedrock
• Internally supported AI development platforms and tooling

This role is not suited to candidates who are dogmatic about specific tools. Adaptability and outcome focus matter more than platform allegiance. Desirable experience

• Working in secure, safety-critical, or heavily regulated environments
• Background in sectors such as energy, oil & gas, defence, or public sector
• Experience within formal technical assurance or governance processes
• Collaboration with external suppliers, partners, or research organisations
• Comfort operating at pace where quality, safety, and compliance are non-negotiable

If you enjoy solving complex problems, applying scientific thinking to messy reality, and delivering ML that stands up to scrutiny, this role offers both challenge and meaning. If interested, apply now!

We’re looking for a Senior Machine Learning Engineer to join a multidisciplinary data and AI team delivering high-impact, real-world solutions in a secure and highly regulated environment.

This is a senior, hands-on practitioner role, not a people-management position. You’ll operate with a high degree of autonomy, leading complex machine learning work end-to-end through technical depth, sound judgement, and delivery credibility.

The organisation is outcome-led rather than technology-led. Where strong market solutions exist, they are used. Machine learning is built in-house only where problems are genuinely complex, niche, or sensitive - requiring experimentation, evaluation, and iteration beyond what can be bought. This means the work is thoughtful, challenging, and purposeful, rather than driven by novelty or trend. What you’ll be doing

You’ll take ownership of complex ML problems, applying scientific thinking and pragmatism in equal measure.

You will:

• Lead end-to-end machine learning delivery, from problem definition through experimentation, evaluation, and iteration