Data Quality & Evaluation Engineer – Medical AI

Epsilon Health; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Epsilon Health in San Francisco is hiring a Research Engineer for the ML Research team to own data quality and evaluation across modeling efforts. You will build filtering and curation systems for clean VLM and classifier training sets, catching label noise and duplicates at scale, and design evaluation methods for radiology report generation with clinical accuracy.

The role partners with radiologists to define quality criteria, develop automated signals, and validate model-based evaluators

Qualifications

• 2+ years in ML, data engineering, or related field.
• Strong Python and software engineering fundamentals.
• Experience building data pipelines and tooling.

Responsibilities

• Build data filtering and curation pipelines for VLM and classifier training sets.
• Develop data quality signals (alignment scoring, automated flagging).
• Collaborate with radiologists to define quality criteria and filters.
• Design evaluation methods for radiology report generation and clinical accuracy.
• Maintain clinical benchmark sets across modalities and difficulty levels.
• Create model-based evaluators and track offline vs. production outcomes.
• Implement continuous evaluation and regression testing across the stack.
• Identify bottlenecks and ship tooling to accelerate research.

Skills

Python

Data pipelines

Research ML

Agile mindset

Tools

Spark

Airflow

Databricks

Job description

Epsilon Health in San Francisco is hiring a Research Engineer for the ML Research team to own data quality and evaluation across modeling efforts. You will build filtering and curation systems for clean VLM and classifier training sets, catching label noise and duplicates at scale, and design evaluation methods for radiology report generation with clinical accuracy.

The role partners with radiologists to define quality criteria, develop automated signals, and validate model-based evaluators