Epsilon Health
Research Engineer - Data Quality & Evals
San Francisco, CA
Sponsorship not specifiedDetected 1 day ago
PythonDatabricksMachine LearningSparkAirflowData EngineeringNLPLLMsManual TestingRadiologyResearch
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About the role
- We're seeking a Research Engineer to join our ML Research team, owning data quality and evaluation across our modeling efforts.
- This is a broad, dynamic role for someone with the agency to identify where the research team is bottlenecked and address it directly.
Responsibilities
- 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.
- 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.
- On the data side, you'll build the filtering and curation systems that keep our VLM and classifier training sets clean - catching label noise, misaligned image-report pairs, duplicates, and quality issues at scale.
- On the evaluation side, you'll design how we measure radiology report generation, incorporating clinical accuracy, completeness, hallucination, and reporting style at once.
- The signals you build feed directly into how our foundation-model and post-training teams train and improve their models.
Requirements
- 2+ years of industry or research experience in ML, data engineering, or a related area
- Comfort working in an ambiguous, fast-moving research environment and collaborating closely with research scientists
- 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
- 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
Nice to have
- 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)
Benefits
- Develop model-based data quality signals (alignment scoring, automated flagging, active-learning loops) to surface the ambiguous or high-value cases worth human review.
Company info
- 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.
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