Recovery Intelligence for health plans. INTELLIVO detects more reimbursement opportunities with no member abrasion. Backed by Thompson Street Capital Partners.
About the role
This is a builder’s role, not a research role. You will write the Python that puts AI models to work on real production problems: reading messy documents and email, resolving entities across systems, enriching records, scoring likelihood, and surfacing signals that were previously invisible.
What they're looking for
- Write clean, production-quality Python that integrates foundation models into automated pipelines and services, similar to our existing document intake, routing, entity resolution, and data enrichment workflows
- Uncover and shape modeling opportunities in our data that lead to stronger recovery outcomes, then build them: classification, entity matching, ranking, and propensity or likelihood scoring
- Design, train, evaluate, and deploy machine learning models using standard modeling and automated machine learning platforms
- Build retrieval and multi-step model workflows using orchestration frameworks, including state handling and guardrails
- Help develop synthetic data approaches where real data is sparse, sensitive, or contractually restricted, including generation strategy and validating that the synthetic data actually improves model performance
- Support the machine learning operations layer: training and inference pipelines, model versioning, deployment automation, and monitoring for drift and performance
More about this role
Sr. AI/Machine Learning Engineer
Minimum Experience Experienced
Role Summary
This is a builder’s role, not a research role. You will write the Python that puts AI models to work on real production problems: reading messy documents and email, resolving entities across systems, enriching records, scoring likelihood, and surfacing signals that were previously invisible.
We are looking for an engineer with good working knowledge of transformer architecture and practical experience with foundation models on both sides of the market: open-source models you can host and run, and commercial models you consume through an API. You do not need to have trained one from scratch. You do need to be comfortable calling them, prompting them well, handling their output, and building reliable services around them.
The role also spans traditional machine learning. We have a large and interesting data set, and part of the job is finding the modeling opportunities hiding inside it that translate into better recovery outcomes: classification, matching, scoring, and prediction. You will also help with synthetic data approaches where real data is limited or contractually restricted. This is a remote...
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