Research Engineer - Data Quality & Evals

• Build data filtering and curation pipelines that keep VLM and classifier training sets clean, detecting label noise, misaligned image-report pairs, duplicates, corrupted studies, and low-quality samples at scale.
• Develop model-based data quality signals (alignment scoring, automated flagging, active-learning loops) to surface the ambiguous or high-value cases worth human review.
• Partner with radiologists and annotators to define quality criteria, adjudicate edge cases, and turn clinical judgment into reusable, scalable filters.
• Design evaluation methodology for report generation that goes beyond surface-level text overlap, measuring clinical accuracy through entity and relation extraction, hallucination and omission rates, and adherence to reporting style.
• Build and maintain clinical benchmark sets, stratified by modality, pathology, and difficulty, with rigorous attention to train/eval contamination.
• Develop and validate model-based evaluators (LLM-as-judge, rubric grading) against radiologist judgment, and track how offline eval correlates with production and clinical outcomes.
• Build continuous evaluation and regression testing so the team can measure every model change quickly and trust the result.
• Work across the research stack (data, training, and evaluation) finding bottlenecks and shipping the tooling that lets research scientists move faster.

• 2+ years of industry or research experience in ML, data engineering, or a related area
• Strong Python and solid software engineering fundamentals; comfortable building tooling and data pipelines from scratch
• Strength in one or both of our core areas, with the willingness to grow into the other:
• Data quality: dataset curation, filtering, deduplication, label-noise detection, or data-centric ML
• Evaluation: designing metrics or eval harnesses for generative models, LLM-as-judge, or NLG / factuality evaluation
• Demonstrated agency, i.e. a habit of identifying important problems and driving them to a result without waiting to be told
• Comfort working in an ambiguous, fast-moving research environment and collaborating closely with research scientists

Preferred Qualifications

• Experience with medical imaging or clinical data (DICOM, radiology reports, clinical NLP)
• Familiarity with vision-language models or multimodal training
• Experience building human-in-the-loop annotation or review workflows, and reasoning about inter-annotator agreement
• Experience with clinical accuracy metrics for report generation (e.g., entity / relation extraction, RadGraph-style scoring)
• Experience with data pipeline and experiment tooling (Spark, Airflow, Databricks, or similar)
• Publications or open-source contributions in data-centric ML, evaluation, or medical AI

We’re tackling one of healthcare’s most critical challenges in medical imaging and diagnostics. Our company operates at the intersection of cutting-edge AI and clinical practice, building technology that directly impacts patient outcomes. We’ve assembled one of the industry’s most comprehensive and diverse medical imaging datasets and have a proven product-market fit with a substantial customer pipeline already in place.