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.

Languages & runtime

• Strong Python experience for NLP pipelines, LLM orchestration, evaluation tooling, and data processing.
• Experience integrating AI systems into production services (Elixir experience is a plus, or willingness to engage deeply with it).

LLM engineering & LLMOps

• Experience with prompt engineering using structured outputs.
• Familiarity with schema-constrained generation (e.g. JSON or ontology-driven outputs).
• Experience designing evaluation and benchmarking frameworks for production LLM systems.
• Understanding of model versioning, regression testing, and iterative improvement cycles.

Knowledge graph integration

• Experience designing AI pipelines that are constrained or validated by graph structures, even if not a formal ontologist.
• Ability to collaborate effectively with Knowledge Engineers to ensure graph representations are AI-usable.
• Understanding of performance and scaling considerations when integrating graph-backed validation.

Operating context

• Experience working in regulated or high-assurance environments is strongly preferred.
• Ability to balance experimentation with production discipline.
• Comfortable operating in a fast-moving startup environment with high ownership expectations.

Personal attributes

• Systems-oriented thinker who values coherence over novelty.
• Pragmatic builder rather than research-focused experimentalist.
• Comfortable taking technical ownership and accountability.
• Strong communicator who documents and disseminates architectural knowledge to avoid bottlenecks.

Benefits

• Competitive Salary
• Company Pension
• 25 days of paid annual leave (pro-rata)
• A fun and flexible hybrid working environment
• Access to our Employee Assistance Programme - Health Assured
• A modern, dog-friendly office located near Chancery Lane with free drinks