Note: The job is a remote job and is open to candidates in USA. Novellia is a healthcare technology company building a patient-powered real-world data platform for longitudinal health insights. The Senior Machine Learning Engineer, Platform will be the first ML hire, owning the end-to-end development and productionization of clinical text extraction models and LLM-based systems. The role also involves defining evaluation standards, building clinical NLP pipelines, partnering on data quality, and shaping the technical roadmap.
Responsibilities
- 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
Skills
- 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
Benefits
- Equity in Novellia
- Medical, dental, and vision coverage
- 401(k)
- Flexible time off
- Wellness stipend
- Up to 12 weeks of parental leave
Company Overview