Data & AI Engineer II

CareDx, Inc. is a leading precision medicine solutions company focused on the discovery, development, and commercialization of clinically differentiated, high-value healthcare solutions for transplant patients and caregivers. CareDx offers products, testing services, and digital healthcare solutions along the pre- and post-transplant patient journey, and is the leading provider of genomics-based information for transplant patients.

The Data & AI Engineer II will develop analytical applications, data pipelines, and AI-powered capabilities that support clinical research, statistical analysis, and data-driven decision-making. This role helps transform research workflows, statistical analyses, and machine learning models into reliable, secure, and user-facing software. The engineer will collaborate with data scientists, statisticians, engineers, clinicians, and product stakeholders to productionize analytical workflows, translate R-based analyses into maintainable Python, and deliver applications for internal and external users. The engineer will also contribute to AI-powered features that analyze structured and unstructured data, extract information from documents, synthesize analytical results, and help users interpret scientific and clinical information. Success in this role requires strong software engineering fundamentals, curiosity, and the ability to independently deliver well-defined components and features with support on more complex technical decisions.

Responsibilities:
• Develop and maintain analytical applications using tools and frameworks such as Python, Streamlit, Plotly, Dash, and Django.
• Own the implementation, testing, deployment, and maintenance of well-defined application features and data workflows.
• Productionize statistical models and machine learning workflows developed by data scientists and statisticians.
• Translate and refactor R-based research and analytical workflows into maintainable, tested Python code.
• Build and maintain data pipelines that support clinical data processing, analysis, visualization, and reporting.
• Develop interactive applications that enable data exploration, visualization, and decision support.
• Integrate applications with relational databases, APIs, analytical outputs, documents, and other structured and unstructured data sources.
• Contribute to AI-powered application features using techniques such as structured information extraction, retrieval-augmented generation, tool or function calling, and multi-step workflows.
• Implement validation, grounding, logging, traceability, and human-review mechanisms for AI-generated outputs.
• Collaborate with scientists, clinicians, product stakeholders, and senior engineers to translate requirements into practical technical solutions.
• Participate in technical design discussions and document implementation decisions, assumptions, and tradeoffs.
• Write reliable, readable, and testable code using established engineering patterns and team standards.
• Participate in code reviews and incorporate feedback to improve implementation quality and technical judgment.
• Monitor, debug, and improve application performance and reliability across data, model, AI, and application layers.
• Follow security, privacy, and compliance requirements for sensitive healthcare data, including HIPAA-aligned practices.
• Contribute to shared components, documentation, development tools, and engineering process improvements.
• Communicate progress, risks, dependencies, and technical challenges clearly and proactively.

Qualifications:
• Education: Bachelor’s or master’s degree in Computer Science, Data Science, Engineering, Biostatistics, or a related quantitative field, or equivalent practical experience.
• Experience: 2+ years of experience in data engineering, backend engineering, full-stack development, analytics engineering, or a related role.
• Strong proficiency in Python for data analysis and software development.
• Experience working with statistical analysis, data science, or machine learning workflows.
• Experience transforming prototype, research, or analytical code into maintainable software.
• Experience building analytical applications using Streamlit, Plotly, Dash, or similar technologies.
• Experience developing backend services or web applications using Django, FastAPI, Flask, or a similar framework.
• Experience building and maintaining data pipelines that integrate multiple data sources.
• Familiarity with relational databases, SQL, APIs, and common data formats.
• Experience or demonstrated applied knowledge in at least one modern LLM capability, such as retrieval-augmented generation, structured extraction, tool calling, or agentic workflows.
• Understanding of core production considerations for AI systems, including grounding, hallucination mitigation, output validation, security, latency, cost, and human oversight.
• Experience with version control and collaborative development practices, including Git, branching, pull requests, and code reviews.
• Experience writing automated tests and debugging applications across multiple technical layers.
• Ability to independently deliver well-defined features while seeking guidance when requirements or technical risks are unclear.