AI software Engineer - Project Tricorder

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About the role

Project Tricorder — Clinical Infrastructure for Field Operations We're looking for a hands-on engineer to build two foundations of the Tricorder product: the evaluation harness and training data pipeline behind our AI models, and a clinical knowledge base grounded in tactical combat care, SNOMED CT and ICD-10. About Project Tricorder - https://www.tricorder-systems.com/ 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. What you'll do You'll own two workstreams, working day to day with the founder and the FF build team. 1. Data pipeline and evaluation harness Build the data foundations that tell us whether our models are good enough for clinical use. • Data labelling: design labelling schemas and guidelines, set up tooling, and run labelling with clinical input, including quality checks and inter-annotator agreement. • Data processing: build reproducible pipelines to ingest, clean, de-identify and transform multimodal data (video, sensor streams, text). • Video data organisation: structure, version and catalogue video datasets so clips, annotations and metadata stay searchable and traceable. • Training dataset preparation: assemble balanced, well-documented train, validation and test splits, with clear lineage from raw data to model input. • Eval harness: build an automated harness that benchmarks vision-language and NLP models against clinically meaningful metrics, tracks regressions and supports build-vs-buy model decisions. 2. Clinical knowledge base Build the structured clinical knowledge base that turns unstructured input into coded, interoperable records. • Database design: design the schema and stand up the data store for clinical concepts and their relationships. • Clinical terminologies: ingest and map TC3, SNOMED CT, ICD-10 and related NHS standards, including cross-mappings between them. • Natural language processing: extract entities and relationships from clinical text and link them to coded concepts. • Document handling: parse and structure clinical documents (PDFs, notes, forms) into the database. • Clinical knowledge: work with clinicians to make sure the database reflects real-world clinical reasoning and pre-hospital workflows. By the end of the first 3 months, we'd expect a working eval harness in use for model decisions, a labelled and versioned core dataset, and a first version of the knowledge base linked to the product. What we're looking for We don't expect one person to be expert in everything below. Strong candidates will be deep in one workstream and credible in the other. Must-haves • Strong Python and data engineering skills, with a track record of shipping production data or ML pipelines, not just research notebooks. • Hands-on experience building ML evaluation frameworks, benchmarks or test harnesses, ideally for vision, video or language models. • Experience running data labelling and training

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