Senior Machine Learning Engineer, Platform

Novellia

1d•$150,000 - $200,000

About The Position

This role is for the first Machine Learning hire at Novellia, joining the Platform Engineering team and reporting to the Head of Platform Engineering. The primary focus will be on transforming unstructured clinical text into trustworthy, structured features, which is a critical capability for making longitudinal health histories usable for research. The role involves approximately 70% applied ML on clinical text (entity extraction, classification, sequence labeling, annotation strategy, error analysis, calibration, evaluation) and 30% LLM-based work (prompt development, structured output, retrieval, evaluation, and observability). The engineer will be responsible for setting technical direction, deciding on extraction strategies, and potentially leading a team as the function grows. This is a hands-on role focused on productionizing ML models rather than pure research.

Requirements

Healthcare or life sciences experience with real clinical data (clinical notes, EHR data, claims, registries, or similar).
6+ years in applied ML, with models personally taken from problem statement to production and maintained.
Depth in applied ML on text: information extraction, NER, classification, sequence labelling, weak supervision, and associated evaluation practices.
Practical, current experience with LLM-based approaches: prompt development, structured output, retrieval, fine-tuning, evals and observability for generative systems.
Strong engineering fundamentals in Python; work runs in production, not just notebooks.
Strong collaboration instincts across the ML boundary: define problems with stakeholders, 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 when evidence supports it.

Nice To Haves

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.

Responsibilities

Own the full lifecycle of extraction models: framing, data/annotation strategy, model selection, training/fine-tuning, evaluation, deployment, monitoring, retraining.
Define "accurate enough" with clinical and customer-facing stakeholders and build a defensible evaluation harness.
Partner with Clinical Data Managers on curation design and own the technical half of QA/QC.
Build clinical NLP pipelines against real-world data and work with backend engineers to productionize them.
Use LLMs with rigor: versioned prompts, real evals, tracked cost/latency, known failure modes.
Make extraction quality legible to non-ML colleagues.
Treat de-identification, PHI handling, audit trails, and access controls as part of the modeling problem.
Help shape the roadmap based on identified problems.