Machine Learning Engineer | Python | PyTorch | OCR | Natural Language Processing | LLM

Machine Learning Engineer | Python | PyTorch | OCR | Natural Language Processing | LLM | Large Language Models | Hybrid, London

Our 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 providers. We believe data handling in current healthcare systems is often complex and disconnected, leading to isolated and inefficient decision‑making. To demonstrate how this technology can advance healthcare delivery and improve lives, we build and deploy products for healthcare providers and payers in the UK and US markets.

We are an energetic health‑tech startup of around forty employees. Our team is growing as we explore new markets and opportunities. We are passionate about applying technology to meaningful challenges. New joiners will have a significant impact on the direction of the company as well as our culture.

Our products are built on a Semantic AI platform that enables payers and providers to access advanced AI capabilities for their own use cases and applications. Partners can use the platform APIs directly or collaborate with us to develop tailored applications.

We develop a suite of products supporting healthcare operations, including an AI tool that helps practices reduce administrative burden in processing clinical correspondence. The system reduces staff time spent identifying codes for medical records and suggests follow‑up tasks and workflow optimisations. It helps providers save time and cost, improve audit performance, and strengthen staffing resilience.

We are seeking an Machine Learning Engineer to join our Applied AI team and work on the clinical document understanding pipeline that underpins our operational AI 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.

• Design, build, and maintain NLP pipelines for clinical document processing using Python.
• Develop and extend pipeline components, including training configurations, packaging, and versioning. Refactor and improve 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, including schema validation, rule‑based checks, and other guardrails.
• Optimise pipeline performance, including latency, throughput, and cost per document.
• Collaborate with Engineering to support production deployment and monitoring of NLP components.

A bachelor’s degree in computer science, computational linguistics, or equivalent educational attainment.

• 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, benchmarks, and regression testing for NLP systems.
• Experience working with healthcare or clinical text.
• Familiarity with clinical terminologies (e.g., SNOMED CT).
• Ex…