Semantic Architect at Dyad AI – London, England

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 into the UK and US markets.

Dyad is an energetic, health-tech startup, currently around forty employees. Our team is growing as we explore new markets and opportunities. We are passionate about technology and its applications in worthwhile ventures. New joiners will have a significant impact on the direction of the company, as well as our culture.

Dyad's Platform: Dyad's products are founded upon our Semantic AI platform, which enables payers and providers to access cutting-edge AI capabilities for their own use cases and applications. Our partners either use the platform APIs directly or work with us to develop applications for their use cases. For more information, please see our Platform page.

Primary care operations: Dyad develops a suite of products for healthcare operations, including:

• BetterLetter, our AI tool helping practices decrease their admin burden in processing clinical letters. We use this to reduce staff time spent identifying codes to be applied to the record as well as suggesting follow-up tasks and workflow optimisations. BetterLetter helps providers save time, save cost, improve performance under audit and build staffing resilience.

Dyad is seeking a Semantic Architect to design and operationalise our graph-integrated generative AI architecture.

This is a senior, hands-on technical leadership role within the Applied AI function. The Semantic Architect is responsible for ensuring that unstructured clinical text, structured knowledge (ontologies and graphs), and generative AI systems work together as a coherent, production-ready system.

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 coordination and is critical to scaling Applied AI delivery without creating single points of technical dependency.

This position is offered on a hybrid basis from our London office.

• 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.

• 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.

• 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.

• 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.

A minimum of a master's degree in computer science with an AI focus or equivalent is required, as well as at least 3+ years commercial experience delivering knowledge-based systems in real production environments.

• 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 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).

• 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.

• Experience designing AI pipelines that are constrained or validated by graph structures.
• 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.

• 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.

• 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.

1. Introductory screening interview (30 minutes)
2. Technical deep-dive interview with Applied AI and Engineering leadership
3. Final interview and offer

• Competitive salary
• Company pension
• 25 days of paid annual leave (pro-rata)
• Flexible hybrid working environment
• Employee Assistance Programme
• Modern, dog-friendly office near Chancery Lane with free drinks