Radiology AI Data Quality & Evaluation Engineer

Epsilon Health; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Epsilon Health in San Francisco seeks a Research Engineer to own data quality and evaluation across ML efforts for radiology report generation. You will build filtering and curation pipelines, create quality signals, and collaborate with radiologists to define criteria.

You will design evaluation methods beyond surface text, measure clinical accuracy, and ship scalable tooling for data, training, and evaluation.

Qualifications

• 2+ years of ML, data engineering, or related experience.
• Strong Python and software fundamentals; able to build tooling and pipelines from scratch.
• Willing to grow in data quality and evaluation areas; comfortable in a fast-moving research setting.

Responsibilities

• Build data filtering and curation pipelines to keep training and evaluation sets clean.
• Develop model-based data quality signals and automated flagging.
• Collaborate with radiologists and annotators to define quality criteria and edge cases.
• Design evaluation methodology for radiology report generation, measuring clinical accuracy and style.
• Create and maintain clinical benchmark sets and regression tests.
• Build evaluators (LLM-as-judge, rubric grading) and track offline vs production outcomes.
• Ship tooling across data, training, and evaluation to speed research.

Skills

Python

Data pipelines

ML engineering

Agency/initiative

Tools

Spark

Airflow

Databricks

Job description

Epsilon Health in San Francisco seeks a Research Engineer to own data quality and evaluation across ML efforts for radiology report generation. You will build filtering and curation pipelines, create quality signals, and collaborate with radiologists to define criteria.

You will design evaluation methods beyond surface text, measure clinical accuracy, and ship scalable tooling for data, training, and evaluation.