Applied AI Engineer / Scientist
IT, Product & Data
About the Role
We are looking for an Applied AI Engineer / Scientist to train, build, evaluate, and continuously improve our production pipelines.
You will work at the intersection of software engineering, model training, LLM systems, evaluation, and deep healthcare workflow understanding. Your role is to put state-of-the-art model capability into production in reliable, auditable, and maintainable pipelines, and continuously improve from real-world failures.
You will be embedded in hard healthcare problems — clinical documentation integrity, autonomous medical coding, denial prevention, appeals, revenue cycle workflows, and payer logic — and will own the loop from problem framing, model training, agents, tools, delivery, evaluation, and improvement.
The ideal candidate is a curious applied scientist with an engineer mindset: rigorous about measurement, comfortable with ambiguity, excited by messy real-world data, and motivated by making things real. You want to close the gap between impressive demos and dependable production systems.
Responsibilities
Support a production AI system end to end, from research to deployment for a product.
Design, build, and iterate on production AI systems for real-world healthcare workflows that demand extreme accuracy and auditability with complex label spaces.
Build evaluation suites that measure system performance across generative, extraction, retrieval, and classification tasks, including multi-label and multi-class classification problems, regressions, edge cases, safety, reliability, provenance quality, and business impact.
Analyze human-in-the-loop feedback data to continuously raise the bar of model performance.
Work with clinical, coding, product, and operations experts to translate domain workflows into scoped production AI systems.
Build feedback loops from expert review, production logs, model outputs, and benchmark runs.
Research and prototype new models and capabilities from the ground up, run evaluations, and move the best into production.
Partner with research scientists and ML engineers on model selection, supervised fine-tuning, reward modeling, distillation, synthetic data generation, or post-training experiments.
Ensure outputs are clinically useful, explainable, and auditable, with clear evidence, source provenance, and decision rationale.
Help define what “good” looks like for AI systems completing complex tasks end to end.