Systems Engineer III (Research)

Key Responsibilities

• Campaign Design & Execution: 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.

• Annotation Process & Tooling: 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.

• Ground-Truth Quality & Reproducibility: Define and monitor quality across data campaigns. Distinguish annotation noise from genuine clinical disagreement, and quantify the reliability of the data you deliver.

• Cross-Functional Collaboration: 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.

• Research Initiative & Data Planning: 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.

• Systems Engineering Best Practices: Promote Systems Engineering best practices across the organization by sharing methods, improving processes, and strengthening systems thinking in data and research initiatives.

Required Qualifications

• Education: Bachelor's or Master's degree (Ph.D. preferred) in Systems Engineering, Computer Science, Data Science, Biomedical Engineering, or a related field.

• Experience: 5+ years of industry experience in research, systems engineering, or a related field, ideally in medical imaging, medical devices, or another regulated domain.

• Program & Delivery Ownership: 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.

• Ground-Truth & Quality Methods: 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.

• Medical Imaging Fluency: Solid understanding of medical image data structures and imaging workflows (e.g., DICOM), as well as the clinical realities that make annotation challenging.

• Data & Tooling Proficiency: Proficiency in Python and SQL for building and validating data pipelines, computing quality metrics, and producing dataset documentation.

• Communication: Exceptional ability to translate research requirements into executable annotation campaigns, and to report dataset quality and provenance clearly to cross-functional stakeholders.

Preferred Qualifications

• 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 developing SaMD products and contributing to regulatory filings.