Senior Machine Learning Engineer, Platform

Most of what matters in a health record isn't in a structured field - it's in the note, the discharge summary, the pathology report, the scanned fax. Turning that unstructured clinical text into trustworthy, structured features is what makes a longitudinal health history usable for research, and it's one of the highest-leverage capabilities Novellia can own.

You'll be our first ML hire, joining Platform Engineering and reporting to the Head of Platform Engineering, as technical owner of this multi-quarter effort. The interesting decisions are still open - what we extract first, how we know we're right, what a mature extraction pipeline looks like at our scale. There's no existing approach to inherit or defend.

The work draws on two toolkits. Roughly 70% is applied ML on clinical text: entity extraction, classification, sequence labelling, annotation strategy, error analysis, calibration, and the evaluation discipline that tells you whether your numbers mean anything. Roughly 30% is LLM-based: prompt development, structured output, retrieval, and the evals and observability that keep generative approaches honest. Deciding which approach a given problem calls for is the most interesting part of the job, and that call is yours.

We're looking for a leader in this seat: setting technical direction rather than waiting to be handed a problem. If this grows the way we think it will, leading the team we build around it is on the table.

Own the full lifecycle of extraction models - framing, data/annotation strategy, model selection, training/fine-tuning, evaluation, deployment, monitoring, retraining. Not a research seat, not a hand-off seat.

Define what "accurate enough" means with clinical and customer-facing stakeholders, and build the evaluation harness that makes the answer defensible - the first deliverable, not a follow-up.

Partner with Clinical Data Managers on curation design and own the technical half of QA/QC alongside them: which variables are extractable, how an instruction becomes a model spec, and the tooling/sampling/error analysis behind human-in-the-loop review.

Build clinical NLP pipelines against messy real-world data and work with backend engineers to productionize what you build.

Use LLMs with the same rigor you'd apply anywhere: versioned prompts, real evals, tracked cost/latency, known failure modes.

Make extraction quality legible to non-ML colleagues, and treat de-identification, PHI handling, audit trails, and access controls as part of the modelling problem, not someone else's checklist.

Help shape the roadmap around the problems you see - a mission and a close working partner, not a backlog.

Healthcare or life sciences experience with real clinical data - clinical notes, EHR data, claims, registries, or similar. This one is not negotiable for us.

6+ years in applied ML, with models you personally took from problem statement to production and kept working - you know what degraded, how you found out, and what you did.

Depth in applied ML on text: information extraction, NER, classification, sequence labelling, weak supervision, and the evaluation practice around them, including annotation guidelines and inter-annotator agreement you've had to act on.

Practical, current experience with LLM-based approaches: prompt development, structured output, retrieval, fine-tuning where warranted, evals and observability for generative systems - enough to know where they help, and where they quietly don't.

Strong engineering fundamentals in Python. Your work runs in production, not only in a notebook.

Strong collaboration instincts across the ML boundary: you define problems with stakeholders before solving them, write clearly, and bring people along.

A track record of solving problems rather than closing tickets. Self-directed, comfortable without a playbook, and comfortable being wrong in public when the evidence says so.

Fluency with clinical terminologies and standards: SNOMED CT, ICD-10, LOINC, RxNorm, CPT, FHIR

Experience with HIPAA, SOC 2, de-identification methodology, or IRB and regulatory-grade data work

Experience as an early or first ML hire

Experience building or running human-in-the-loop annotation and QC operations at scale

OCR and document-understanding experience on low-quality real-world documents

Experience mentoring or leading ML engineers, or interest in growing that way

Not a research role. The bar is extraction quality in production, not publications.

Not an LLM-wrapper role. If your instinct is that every problem is a prompt away from being solved, we'll frustrate each other.

Not a large-team role yet. You'd be the first ML engineer in a small Platform Engineering function - breadth and influence, and fewer specialists to lean on.

Not a role where someone hands you a clean labelled dataset. Building it is the job.

Ground-floor ownership of a capability with direct commercial weight, with influence over architecture, roadmap, and eventually hiring.

Both halves of the modern ML toolkit in one seat, on a problem where the choice between them genuinely matters.

A manager who treats process and people work as legitimate engineering work, and intends for this seat to grow.

Health tech means the work has stakes - better extraction means higher quality research