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.
Our products
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.
The role
Dyad is seeking an NLP Engineer to join our Applied AI team and work on the clinical document understanding pipeline that underpins BetterLetter and related products.
This is a hands‑on engineering role focused on building, improving, and maintaining production NLP systems. You will work on OCR‑aware document processing, entity extraction and linking, and the safe integration of LLM components within a constrained, regulated architecture.
The role is offered on a hybrid basis from our London office.
Core responsibilities
- Design, build, and maintain NLP pipelines for clinical document processing using Python.
- Develop and extend pipeline components as well as training configurations, packaging, and versioning. Refactor and improve pipeline components for maintainability, scalability, and clarity.
- Train, evaluate, and deploy NLP and OCR models for clinical concepts. Maintain evaluation datasets and implement regression testing for model and pipeline updates.
- Improve document structure detection, sectioning, and layout‑aware extraction, particularly for scanned documents.
- Enhance handling of negation, temporality, and related concepts in clinical text.
- Analyse production errors and implement targeted improvements to reduce recurring extraction and coding issues.
- Integrate LLM‑based components into the pipeline using structured inputs and validated outputs. This includes implementing schema validation, rule‑based checks, and other guardrails around model outputs.
- Optimise pipeline performance, including latency, throughput, and cost per document.
- Collaborate with Engineering to support production deployment and monitoring of NLP components.
Requirements
Experience & technical background
- Strong professional experience in applied NLP and machine learning engineering.
- Advanced Python skills, including experience building and maintaining production ML systems.
- Hands‑on experience with common NLP frameworks.
- Experience training and evaluating NER and/or entity linking models.
- Experience working with noisy or unstructured text data, such as OCR‑derived documents.
- Familiarity with combining rule‑based and statistical approaches in production systems.
- Experience designing and implementing evaluation metrics and benchmarks as well as regression testing for NLP systems.
Desirable experience
- Experience working with healthcare or clinical text.
- Familiarity with clinical terminologies such as SNOMED CT.
- Experience integrating LLMs into structured application pipelines.
- Experience working in regulated or high‑assurance environments.
- Exposure to hybrid symbolic and generative AI architectures.
Personal attributes
- Detail‑oriented with a strong focus on accuracy and reliability.
- Pragmatic approach to problem‑solving, selecting appropriate techniques for the task.
- Comfortable working in a fast‑paced startup environment.
- Strong communication skills and ability to work effectively within a multidisciplinary team.
Our hiring process
- Introductory screening interview (30 minutes)
- Technical deep‑dive interview with Applied AI and Engineering leadership
- Final interview and offer
Benefits
- 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
#J-18808-Ljbffr