> Markdown version of [/jobs/ext/2280204-machine-learning-engineer-platform](https://www.wearedevelopers.com/jobs/ext/2280204-machine-learning-engineer-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer, Platform - **Company:** Novellia, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $150,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Audit Trail, Clinical Data Repository, Information Extraction, Python (Programming Language), Named Entity Recognition, Fast Healthcare Interoperability Resources, Large Language Models, Backend - **Published:** August 28, 2026 - **Apply:** https://www.builtincolorado.com/auth/login?destination=/job/senior-machine-learning-engineer-platform/10851999 ## About the Role * 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. ## Related Videos - [Creating Industry ready solutions with LLM Models](https://www.wearedevelopers.com/videos/899-creating-industry-ready-solutions-with-llm-models) - [Outclassing Frontier LLMs at Extracting Information](https://www.wearedevelopers.com/videos/100303-outclassing-frontier-llms-at-extracting-information) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Resilient by Design: Building Robust Architectures in High-Stakes Financial Systems](https://www.wearedevelopers.com/videos/2106-resilient-by-design-building-robust-architectures-in-high-stakes-financial-systems) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [7 Most Popular Web Developer Jobs in Europe](https://www.wearedevelopers.com/magazine/163-7-most-popular-web-developer-jobs-in-europe)