Systems Engineer III (Research)

Heartflow•San Francisco, CA

5d•$150,000 - $185,000

About The Position

Heartflow is a medical technology company advancing the diagnosis and management of coronary artery disease, the #1 cause of death worldwide, using cutting-edge technology. The flagship product—an AI-driven, non-invasive cardiac test supported by the ACC/AHA Chest Pain Guidelines called the Heartflow FFRCT Analysis—provides a color-coded, 3D model of a patient’s coronary arteries indicating the impact blockages have on blood flow to the heart. Heartflow is the first AI-driven non-invasive integrated heart care solution across the CCTA pathway that helps clinicians identify stenoses in the coronary arteries (RoadMap™Analysis), assess coronary blood flow (FFRCT Analysis), and characterize and quantify coronary atherosclerosis (Plaque Analysis). Our pipeline of products is growing and so is our team; join us in helping to revolutionize precision heartcare. Heartflow is a publicly traded company (HTFL) that has received international recognition for exceptional strides in healthcare innovation, is supported by medical societies around the world, cleared for use in the US, UK, Europe, Japan and Canada, and has been used for more than 500,000 patients worldwide. We are looking for a Systems Engineer III to own Heartflow's research data pipeline. In this role, you will generate the data that enables current and future algorithm and product research. You will partner with Data Scientists, Research Scientists, and engineering teams to understand our products, systems, and constraints, then determine how future data campaigns should be collected, annotated, and analyzed. Trustworthy data is fundamental to everything we do at Heartflow. It informs our research and helps us improve and transform our algorithms and products. In this role, your work will turn complex clinical and product questions into reliable, well-documented datasets that move research forward. If you are drawn to solving systematic problems, passionate about research, and deeply committed to high-quality data, then this is the role for you.

Requirements

Bachelor's or Master's degree (Ph.D. preferred) in Systems Engineering, Computer Science, Data Science, Biomedical Engineering, or a related field.
5+ years of industry experience in research, systems engineering, or a related field, ideally in medical imaging, medical devices, or another regulated domain.
Proven track record translating complex projects or research needs into clear, executable programs and driving them from planning through delivery. Experience coordinating cross-functional teams and delivering complete, documented outputs.
Working knowledge of methods for evaluating data and experiment quality, including reproducibility, agreement, and error detection, along with the judgment to distinguish protocol or annotation noise from true clinical variability.
Solid understanding of medical image data structures and imaging workflows (e.g., DICOM), as well as the clinical realities that make annotation challenging.
Proficiency in Python and SQL for building and validating data pipelines, computing quality metrics, and producing dataset documentation.
Exceptional ability to translate research requirements into executable annotation campaigns, and to report dataset quality and provenance clearly to cross-functional stakeholders.

Nice To Haves

Experience running annotation or ground-truth campaigns for medical imaging AI, especially CT or cardiovascular imaging (CCTA).
Familiarity with reproducibility and agreement statistics (e.g., intraclass correlation, Bland-Altman) as they apply to reader studies and ground truth.
Experience with large-scale data querying using SQL and cloud storage such as AWS.
Experience developin

Responsibilities

Own data campaigns for research. Define labeling objectives, annotation schemas, sample selection and inclusion criteria, and acceptance criteria in partnership with Data and Research Scientists. Plan and track timelines and resourcing to drive each campaign from ideation to delivery.
Own the annotation process end to end, including the workflow, software, and steps that move data through it (ingest, schema mapping, format and ontology validation, and delivery). Select and configure the right tools, and build a reproducible process that removes bottlenecks and scales campaigns across diverse research objectives.
Define and monitor quality across data campaigns. Distinguish annotation noise from genuine clinical disagreement, and quantify the reliability of the data you deliver.
Partner with Data Scientists, Research Scientists, Process Engineering, Product, Clinical, and Regulatory teams. Turn campaigns into artifacts other functions consume: documented datasets, quality reports, and dataset records reproducible and auditable enough to support algorithm development and product decisions.
Translate research initiatives into clear data requirements, including target populations, modalities, labels, quality thresholds, and delivery timelines. Trace each requirement to a planned or active campaign, identify gaps and dependencies early, and coordinate priorities to ensure the right data is available when research needs it.
Promote Systems Engineering best practices across the organization by sharing methods, improving processes, and strengthening systems thinking in data and research initiatives.