Senior Data & AI Engineer

CareDx, Inc. is hiring a Senior Data & AI Engineer to transform structured and unstructured real-world EMR data into reliable, standardized clinical data. The role designs and delivers data engineering and clinical NLP pipelines, integrating these sources into a transplant-focused OMOP Common Data Model for clinical, scientific, and business operations.

Responsibilities:

• Own the design, implementation, testing, deployment, and maintenance of pipelines that integrate structured EMR data and process unstructured clinical narratives
• Develop entity and relationship extraction workflows for diagnoses, symptoms, laboratory results, demographics, medications, allergies, and procedures, linking findings to relevant values, units, doses, routes, frequencies, anatomical sites, and dates
• Normalize structured source codes and extracted clinical concepts using ICD, SNOMED CT, RxNorm, LOINC, and other appropriate terminologies; manage local codes, synonyms, abbreviations, ambiguous mappings, terminology versions, and hospital-specific language
• Implement clinical context interpretation, including negation, uncertainty, temporality, experiencer, and treatment status. Distinguish current conditions from historical, family, hypothetical, and ruled-out findings
• Handle clinical note structure, copied-forward content, conflicting statements, and information spanning sentences or encounters. Reconcile extracted findings with structured EMR records using defined source and time precedence rules while retaining discrepancies, source evidence, and clinical timelines
• Build and evaluate hybrid approaches using rules, dictionaries, statistical or deep learning models, and LLMs; apply structured output validation and terminology checks to prevent unsupported facts and invalid codes
• Partner with clinicians to define clinical data schemas, source mappings, annotation guidelines, reference datasets, and acceptance criteria; resolve annotation disagreements and translate clinical requirements into engineering specifications
• Measure entity, relationship, assertion, and normalization performance; conduct clinical error analysis and validate generalization across hospitals, specialties, and note types using patient-level data separation and checks for data leakage
• Preserve source text, note and encounter identifiers, extraction provenance, model and terminology versions, and review status. Establish confidence thresholds and human review workflows for ambiguous or high-impact outputs
• Build ETL workflows, APIs, and validation controls to harmonize structured EMR fields and NLP outputs, including patient and encounter linkage, units, timestamps, and duplicate records; monitor data quality, throughput, reliability, and model performance, and resolve production issues
• Document architecture and operational procedures, lead code and design reviews, mentor engineers, and communicate technical tradeoffs to clinical, scientific, product, and engineering stakeholders
• Follow security, privacy, and compliance requirements for sensitive healthcare data, including HIPAA-aligned practices, least-privilege access, appropriate logging, and secure data handling
• Design and maintain mappings and transformation pipelines for a transplant-focused OMOP CDM, representing transplant procedures, donor and recipient information, immunosuppressive therapies, laboratory results, rejection episodes, and longitudinal outcomes using appropriate standard concepts and documented extensions where needed

Requirements:

• Master's or Ph.D. degree in Computer Science, Computational Linguistics, Biomedical Informatics, Data Science, Engineering, or a related field, or equivalent practical experience
• 7+ years of relevant experience in NLP, machine learning, or applied AI engineering, including 3+ years developing clinical NLP solutions using real-world EHR or hospital notes, with demonstrated delivery of production solutions
• Advanced Python and SQL skills, with experience using pandas or comparable data-processing tools, relational data modeling, ETL orchestration, incremental processing, schema validation, and automated data-quality checks
• Hands-on experience integrating structured EMR data through HL7 FHIR, HL7 v2, APIs, or database extracts; ability to align patient and encounter records, normalize units and timestamps, manage duplicates, and reconcile conflicting clinical sources
• Hands-on experience with the OMOP Common Data Model and OHDSI standardized vocabularies, including source-to-standard concept mapping, vocabulary relationships, and ETL development to transform structured EMR data and clinical NLP outputs into appropriate OMOP tables
• Experience validating OMOP data for conformance, completeness, and plausibility, with traceable source mappings and quality controls for longitudinal clinical records
• Hands-on experience with clinical entity recognition and linking, relationship extraction, assertion detection, and temporal extraction, including clinical sentence and section detection, abbreviation disambiguation, negation, uncertainty, and experiencer identification
• Experience implementing clinical NLP pipelines using medspaCy, scispaCy, cTAKES, or comparable frameworks, combining rules, dictionaries, and machine learning based on task requirements and measured performance
• Practical expertise with ICD, SNOMED CT, RxNorm, LOINC, and UMLS, including terminology services, value sets, semantic types, ontology hierarchies, concept retrieval and disambiguation, local-code crosswalks, and terminology version management; experience with unit normalization using UCUM or equivalent methods
• Experience applying and evaluating transformer models using PyTorch, TensorFlow, or equivalent frameworks and Hugging Face Transformers or comparable tooling; hands-on experience with LLM-based clinical extraction, schema-constrained outputs, and terminology-grounded validation
• Strong understanding of medical terminology, common diseases, laboratory measurements, medications, and clinical workflows
• Ability to apply clinical reasoning to interpret documentation, distinguish evidence from inference, select appropriate concept specificity, and recognize ambiguous findings requiring clinician review
• Experience creating clinician-reviewed reference datasets and annotation guidelines; measuring precision, recall, and F1 for extraction, normalization, and assertion tasks; preventing patient-level data leakage; and evaluating performance across source systems and note types
• Experience building production APIs or services, deploying containerized applications on a cloud platform, and using Git, automated testing, CI/CD, model and data versioning, monitoring, batch inference, error recovery, and performance testing
• Demonstrated ability to lead technical work independently, mentor engineers, document designs and operational procedures, and communicate tradeoffs clearly to clinicians, clinical informaticists, scientists, data engineers, and business stakeholders
• Experience with transplant clinical data or research, including donor and recipient characteristics, immunosuppressive therapies, graft function, rejection, and longitudinal outcomes
• Experience with OHDSI tools such as Usagi, Achilles, or Data Quality Dashboard, and designing documented OMOP extensions when standard concepts or structures do not adequately represent domain requirements
• Experience adapting or fine-tuning biomedical or clinical language models, active learning, distributed processing, and optimizing inference for high-volume clinical data
• Experience with OCR and document layout processing for scanned clinical records, including assessment of how OCR errors affect extraction quality