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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer, Platform - **Company:** Novellia, Inc. - **Location:** United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Audit Trail, Clinical Data Repository, Data Files, Information Extraction, Python (Programming Language), Machine Learning, Named Entity Recognition, Fast Healthcare Interoperability Resources, Large Language Models, Backend - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-engineer-platform--f4ccfa78-f3aa-42f1-b913-343a30698c88 ## About the Role Machine Learning, Healthcare, Medical Records, Funding, Data Sets, Regulatory Submissions, Pathology, Calibration, Strategic Analysis, Team Lead/Manager, * 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. Nice to have * 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 ## Description 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. What you'll do * 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.