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HeartFlow is a publicly traded medical technology company (HTFL) pioneering AI-driven solutions for coronary artery disease diagnosis and management. The company's flagship product, HeartFlow FFR_CT Analysis, uses cutting-edge AI to generate 3D models of coronary arteries and assess blood flow impact—a non-invasive alternative to traditional cardiac testing. The product is cleared in the US, UK, Europe, Japan, and Canada, and has been used by over 500,000 patients worldwide.
We seek a Systems Engineer III to own and lead HeartFlow's research data pipeline. This role is central to generating high-quality datasets that enable current and future algorithm and product research. You will partner closely with Data Scientists, Research Scientists, and engineering teams to translate complex clinical and product questions into reliable, well-documented datasets.
Key responsibilities include:
**Campaign Design & Execution:** Own end-to-end data campaigns for research. Define labeling objectives, annotation schemas, sample selection criteria, and acceptance thresholds in partnership with research teams. Plan and track timelines and resourcing from ideation through delivery.
**Annotation Process & Tooling:** Manage the complete annotation workflow, including software selection, schema mapping, format validation, and delivery. Build reproducible, scalable processes that eliminate bottlenecks across diverse research objectives.
**Ground-Truth Quality & Reproducibility:** Define and monitor data quality across campaigns. Distinguish annotation noise from genuine clinical disagreement, and quantify data reliability using appropriate statistical methods.
**Cross-Functional Collaboration:** Work with Data Scientists, Research Scientists, Process Engineering, Product, Clinical, and Regulatory teams. Deliver documented datasets, quality reports, and reproducible dataset records that support algorithm development and product decisions.
**Research Initiative & Data Planning:** Translate research initiatives into clear data requirements (target populations, modalities, labels, quality thresholds, timelines). Identify gaps and dependencies early; coordinate priorities to ensure data availability aligns with research needs.
**Systems Engineering Best Practices:** Champion systems engineering methodologies across the organization, improving processes and strengthening systems thinking in data and research initiatives.
Required qualifications: Bachelor's or Master's degree (Ph.D. preferred) in Systems Engineering, Computer Science, Data Science, Biomedical Engineering, or related field. 5+ years of industry experience in research, systems engineering, or related fields, ideally in medical imaging, medical devices, or regulated domains. Proven track record translating complex projects into executable programs and driving delivery. Working knowledge of data quality evaluation methods (reproducibility, agreement, error detection). Solid understanding of medical image data structures and workflows (e.g., DICOM). Proficiency with data tools and annotation platforms.
