Head of AI

About Dyad

Dyad's mission is to improve the delivery and efficiency of healthcare.

We are building a platform to model and manage the flow of information within healthcare organisations, improving outcomes for patients, payers, and healthcare providers. We believe data handling in current healthcare systems is needlessly complex and disconnected, leading to isolated and inefficient decision making. To showcase how this technology can advance the delivery of healthcare and improve lives, we build and deploy products for healthcare providers and payers across the UK and US markets.

Dyad is an energetic, early-stage startup of around twenty people. Our team is growing as we explore new markets and opportunities. We are passionate about technology and its application to meaningful, real-world problems. New joiners have a significant impact on both the direction of the company and its culture.

Our products

Dyad Platform

Dyad's products are founded on our Semantic AI platform, combining knowledge graphs and generative AI to deliver grounded, explainable intelligence for healthcare workflows.

Primary Care Operations

Dyad develops a suite of products supporting healthcare operations, including:

BetterLetter — an AI tool that helps GP practices reduce administrative burden when processing clinical correspondence. BetterLetter supports clinical coding, follow-up task identification, and workflow optimisation, helping practices save time and cost, improve audit performance, and build operational resilience.

The role

Dyad is seeking a Head of AI to lead a team that designs and operationalises our graph-integrated generative AI architecture.

This is a senior, hands-on technical leadership role within the Applied AI function. The Head of AI is responsible for building and leading a team that builds production systems handling unstructured clinical text, structured knowledge (ontologies and graphs), and generative AI. Dyad has a learning and teaching culture and the candidate for the role should be as comfortable coming up with accessible explanations for stakeholders and sharing knowledge with team members as they are digging into technical questions.

This role spans a number of disciplines within the ML, NLP, and AI disciplines and is not just another LLM-wrapper position. If your experience is solely around using LLMs within the AI space, this role will not be for you; it is important that a candidate for this role to have broad and integrative understanding of the deep technical foundations of language and machine learning, touching on and including everything from mathematical statistics to computational linguistics, machine learning architectures to system design and evaluation, as well as an understanding of the current state of the art in generative systems.

You will bridge NLP pipelines, LLM-based reasoning, and knowledge graph grounding to produce outputs that are accurate, explainable, and suitable for use in regulated healthcare environments. The role combines architectural ownership with day-to-day technical leadership and is critical to scaling our Applied AI delivery.

This position is offered on a hybrid basis from our London office. Note that candidates must be in the UK or planning on an immediate relocation or their application will not be considered.

Core Responsibilities

Technical leadership & architecture ownership

Design and own end-to-end AI architectures that integrate:
NLP pipelines
LLM-based reasoning and orchestration
Pipeline evaluations and benchmarking
Knowledge graph grounding and validation
Define how structured semantics constrain, validate, and guide generative outputs
Make pragmatic architectural decisions balancing accuracy, performance, explainability, and engineering effort
Set standards for system design patterns across the Applied AI stack
Ensure AI features are production-ready, robust, and aligned with product intent
Day-to-day technical coordination

Coordinate technical work within the Applied AI team
Break product requirements into coherent, technically sound implementation plans
Ensure alignment between NLP components, graph systems, and application layers
Maintain architectural coherence as features evolve and scale
Represent Applied AI in cross-functional technical discussions with Engineering and Product

Evaluation, benchmarking & quality

Define and maintain evaluation frameworks for:
Hallucination detection
Precision and recall of extracted clinical concepts
Regression testing across model updates
Implement structured output approaches (e.g. schema-constrained generation, ontology-driven formats)
Design iterative feedback loops, including human-in-the-loop review where appropriate
Ensure measurable improvements in grounding, explainability, and reliability over time
Compliance-aware AI engineering

Design AI workflows that embed traceability, auditability, and data minimisation by default
Ensure architectural decisions align with medical device and data protection requirements across UK and US contexts
Work proactively with Clinical Safety and QARA teams to avoid late-stage architectural risk

Requirements

Requirements

A minimum of a master's degree in computer science with an AI focus or equivalent is required, as well as at least 5+ years commercial experience delivering production AI/NLP systems, with experience operating at architectural or technical leadership levels.

Core technical expertise

Strong hands-on experience in designing production AI systems that integrate LLMs with structured knowledge
Deep understanding of trade-offs between symbolic reasoning, probabilistic inference, and generative pattern matching
Experience building systems that combine NLP pipelines with structured data validation or knowledge graphs
Strong background in clinical NLP, entity recognition, and terminology mapping (SNOMED CT, ICD, UMLS).
Experience designing document AI systems using OCR, layout-aware models, or multimodal architectures

Lan