Director of AI Systems & Foundation Models

What this person actually does

• Leads a high-calibre team of AI scientists and ML engineers building large-scale, production-ready AI systems

• Designs and delivers foundation models using self-supervised and representation learning techniques

• Builds models that learn across multiple data modalities and scales, from granular signals through to population-level insights

• Embeds explainability, uncertainty and causal reasoning into model design, not as an afterthought

• Partners closely with downstream teams to ensure models can be fine-tuned, deployed and deliver real-world impact

• Makes clear, defensible build vs adapt vs buy decisions across models, tooling and external partnerships

• Sets standards for model evaluation, bench-marking, validation and data quality

• Represents the organisation externally through publications, open-source contribution or recognised thought leadership

What success looks like

• Reusable AI systems that are actively leveraged across multiple high-value applications

• Models that improve as new data modalities are introduced

• Clear translation from research into deployed capability

• Strong internal confidence in model outputs due to explainability and uncertainty awareness

• A motivated, high-performing team with strong retention and progression

The kind of background that fits

• Deep expertise in modern machine learning, especially foundation models, self-supervised learning or representation learning

• Strong understanding of neural network architectures, multi-modal learning and transfer learning

• Experience with distributed training, large-scale model optimisation and efficient fine-tuning

• Proven track record of research impact (top-tier publications, widely adopted systems or equivalent)

• Comfortable working with imperfect, real-world data rather than curated academic data sets

• Experience translating advanced research into production-grade systems

• Senior leadership experience, including leading leaders, hiring at scale and owning capability road maps

• Confident communicator who can bridge technical depth and strategic clarity with senior stakeholders

Mindset and style

• Comfortable with ambiguity and research uncertainty

• Commercially and impact-oriented, not research-for-research's-sake

• Curious, rigorous and opinionated, but open to challenge

• Sets a high bar for quality, documentation and reproducibility

• Motivated by building things that matter, not just publishing them

Why this role is compelling

This is a rare opportunity to operate at the intersection of cutting-edge AI and meaningful real-world application, with the scope to define how advanced machine learning systems are designed, evaluated and used at scale.

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