# Senior Machine Learning Engineer, Platform at Novellia

- Company: Novellia
- What the company does: Novellia is a patient data and healthcare technology company that develops a platform for collecting, organizing, and unifying personal medical information across multiple healthcare providers. Backed by Khosla Ventures.
- Company website: https://novellia.com
- Type: Startups (AI role)
- Level: Senior
- Location: Remote
- Work setup: Remote
- Pay: $150K to $200K base salary per year (USD)
- Posted: 2026-08-25
- Apply by: 2026-10-14
- Apply: https://jobs.ashbyhq.com/novellia/b78bdb55-a439-453b-bf62-a670b31f1fb0
- Page: https://www.1752.vc/careers/jobs/novellia-senior-machine-learning-engineer-platform/

## About the role

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.

## What they're looking for

- 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

Tags: Technology