Research Data Operations Engineer
About the job
- A university degree in Computer Science or a related field is required.
- At least 3 years of relevant work experience in a similar role and field is required.
- At least 2 years of experience in the healthcare sector is required.
- Excellent verbal and written English communication skills are required.
- Strong analytical and problem-solving abilities are required.
- The ability to work independently and as part of a team is required.
- Strong Python skills are required; Rust is ideal, and R is a plus.
- Solid data engineering fundamentals, including pipelines, file and format handling, validation, and versioning, are required.
- A high standard of correctness, reproducibility, and data integrity is required.
- Experience handling sensitive or regulated data, or the discipline to get up to speed quickly, is required.
- Several years of experience building and operating data tooling and infrastructure are required.
- Create and support tools for extracting, matching, and transforming electronic health record data.
- Support data versioning with DVC, making dataset updates, tracking, and rollbacks routine.
- Build and maintain pipelines and tools to clean, validate, and organize clinical imaging data and electronic health record excerpts.
- Support migration from the custom image format to DICOM.
- Make data annotation fast and straightforward for researchers.
- Set up and manage access controls so authorized people can access the appropriate data.
- Keep data handling aligned with clinical and privacy regulations.
- Build search and statistics tools so the team can locate datasets and retrieve summary statistics quickly.
- Own data integrity across the full data lifecycle.
- DICOM or medical imaging experience is desirable.
- Hands-on experience with DVC or similar data-versioning tools is desirable.
- Rust experience in production, deployed in a professional environment, is desirable.
- Experience in regulated environments such as medical, clinical, GDPR, or PHI contexts is desirable.
About the company
RetinAI Medical provides an AI-powered healthcare data platform that harmonizes and analyzes unstructured medical data from formats like DICOM and proprietary systems. It helps medical professionals, researchers, and healthcare organizations manage, access, and analyze data to reveal insights about disease progression and treatment outcomes. The platform supports data collection, processing, visualization, and global collaboration through a subscription model and partnerships with clinics and research institutions. Its goal is to help healthcare providers make data-driven decisions that improve patient outcomes.
Company Size
11-50
Company Stage
Series B
Total Funding
$16.7M
Headquarters
Bern, Switzerland
Founded
2016
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Simplify's Take
What believers are saying
- LuxIA won CE-MDR Class IIb certification on April 30, 2025, expanding regulated use.
- June 24, 2024 MDR certification covered four devices, strengthening RetinAI's compliance moat.
- EssilorLuxottica's October 15, 2025 acquisition opens hospital, pharma, and retail eye-care channels.
What critics are saying
- Swiss study in April 2026 found server-hosting, anonymization, and interoperability barriers.
- RetinAI Discovery's automated AI modules remain research-use-only in the United States, limiting adoption.
- By 2027, EssilorLuxottica can absorb RetinAI into its stack, killing standalone identity.
What makes RetinAI Medical unique
- RetinAI Discovery holds FDA 510(k) clearance and CE marking, rare in ophthalmic AI.
- Its platform unifies OCT, fundus, and biomarker data for clinics and pharma partners.
- EssilorLuxottica acquired RetinAI on October 15, 2025, giving global distribution leverage.
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Benefits
- Hybrid Work Options
- Professional Development Budget
- Conference Attendance Budget