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About the Role
Project Tricorder is building deployable clinical infrastructure for field operations, serving defense medical and civilian pre-hospital care. This role focuses on building the evaluation harness and training data pipeline for AI models, and a clinical knowledge base grounded in tactical combat care, SNOMED CT, and ICD-10.
Responsibilities
- Build the data foundations that tell us whether our models are good enough for clinical use.
- Design labelling schemas and guidelines, set up tooling, and run labelling with clinical input, including quality checks and inter-annotator agreement.
- Build reproducible pipelines to ingest, clean, de-identify and transform multimodal data (video, sensor streams, text).
- Structure, version and catalogue video datasets so clips, annotations and metadata stay searchable and traceable.
- Assemble balanced, well-documented train, validation and test splits, with clear lineage from raw data to model input.
- Build an automated harness that benchmarks vision-language and NLP models against clinically meaningful metrics, tracks regressions and supports build-vs-buy model decisions.
- Design the schema and stand up the data store for clinical concepts and their relationships.
- Ingest and map TC3, SNOMED CT, ICD-10 and related NHS standards, including cross-mappings between them.
- Extract entities and relationships from clinical text and link them to coded concepts.
- Parse and structure clinical documents (PDFs, notes, forms) into the database.
- Work with clinicians to ensure the database reflects real-world clinical reasoning and pre-hospital workflows.
Requirements
- Strong Python and data engineering skills, with a track record of shipping production data or ML pipelines.
- Hands-on experience building ML evaluation frameworks, benchmarks or test harnesses, ideally for vision, video or language models.
- Experience running data labelling and training-data preparation, including annotation tooling and quality control.
- Working knowledge of databases and data modelling.
- Practical NLP experience: entity extraction, entity linking or document parsing.
- Comfort handling sensitive data under data protection and clinical safety constraints.
- Pragmatism and speed: ability to work with ambiguity, make good build-vs-buy calls and ship in weeks, not quarters.
Skills
- Python
- Data Engineering
- ML Evaluation Frameworks
- ML Benchmarks
- ML Test Harnesses
- Vision Models
- Video Models
- Language Models
- Data Labelling
- Training Data Preparation
- Annotation Tooling
- Quality Control
- Databases
- Data Modelling
- NLP
- Entity Extraction
- Entity Linking
- Document Parsing
- SNOMED CT
- ICD-10
- NHS Clinical Terminologies
- FHIR
- HL7
- LLMs
- VLMs
- Edge Deployment
- On-device Deployment
Work Type
- Consultancy agreement
- Permanent employment contract
Experience Level
- Early-stage startup experience
- Founding or early engineer experience
Salary/Compensations
- Competitive day rate (consultancy)
- Shares as part of the package (permanent)
About the Company
- Most healthcare technology assumes a hospital: reliable power, connectivity and a clinician at a desk. Care increasingly happens somewhere else - in the field, in transit, in remote and contested environments, and in the critical minutes before a patient reaches a ward.
- Tricorder builds the deployable clinical infrastructure for that world: a rugged, self-contained hardware and software platform that lets clinical teams capture, monitor and act on patient data anywhere, even when the network or the grid can't be relied on.
- It is a dual-use venture serving defence medical and civilian pre-hospital care.
- Our prototype product is already in user testing.
- Edge AI sits at the core: multimodal language models that turn video, sensor data and clinical notes into patient records, drive predictions and inform data-driven learnings across deployments.
- To trust those models in the field, we need rigorous data and evaluation - that's where you come in.