Research Engineer / Research Scientist, Data Quality & Evaluations

What you'll do

• Develop scalable data filtering, curation, and quality pipelines; detect dataset issues such as label noise, misalignment, and duplicates.
• Design evaluation frameworks and automated or human-in-the-loop systems for radiology report generation, including clinical accuracy and hallucination assessment.
• Build benchmarks, evaluation infrastructure, and continuous regression testing in collaboration with researchers and radiologists.

What they ask for

• Strong expertise in machine learning, data-centric AI, model evaluation, or research engineering.
• Demonstrated ability to build robust data pipelines, evaluation systems, or ML research infrastructure.
• Strong Python programming skills and ability to develop reliable, scalable research tooling.
• Experience in dataset curation and quality or evaluation of generative and multimodal models.

Nice to have

• Experience developing evaluation frameworks for LLMs, VLMs, or other generative models.
• Familiarity with data filtering, deduplication, label-noise detection, or active learning.

Tools

Python, LLMs, VLMs, Spark, Airflow, Databricks

AI focus

Advance data curation and evaluation for medical-imaging AI, including model-based evaluators, multimodal evaluation, and clinical reliability assessment.