NLP Engineer

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

• Minimum of a bachelor’s degree in computer science, computational linguistics, or equivalent educational attainment.
• At least 2 years of commercial experience. This is not a graduate role.
• 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.
• 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.

Benefits

• 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.

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