AI Healthcare Software Engineer for Longitudinal Data

RemoteFull-time contractAdvanced4040h est.

Opportunity Summary

This opportunity covers multiple high-ownership engineering tracks for an eye care health AI platform. The roles include a software engineer building product and infrastructure from early stages, a mobile engineer owning end-to-end app UX and capturing high quality data, and an AI engineer building the vision pipeline that converts smartphone photos into clinical signals plus LLM reasoning over patient history. A key differentiator is access to a proprietary longitudinal dataset created through controlled capture, enabling improvements to both data quality and model performance. You will collaborate with clinician experts to define ground truth and validate outputs for safety and usefulness.

Ideal Candidate

An engineer who can work end to end with real user and clinical data, from capture to inference to evaluation. Comfortable partnering with clinicians and iterating quickly based on validation feedback and data quality findings.

Project Insights

  • Emphasize dataset-driven improvements, data quality wins often drive better models
  • If applying to backend, highlight infra for data pipelines and agentic workflows
  • If applying to mobile, show UX decisions that improve capture reliability

How to Prepare

  • Prepare a case study tied to quality validation, not just model accuracy
  • Bring examples of handling noisy sensor or image data and improving capture pipelines
  • Be ready to explain how you measure clinical usefulness and reduce failure modes

Typical Challenges

  • Building robust vision pipelines from smartphone-captured clinical images
  • Ensuring longitudinal data quality and consistent capture across time
  • Connecting clinical ground truth to model training and evaluation workflows

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Project Details

Project sizeLarge
TimelinePermanent position
Hours estimate40–40 hours
ExperienceMid
EngagementFull-time contract

Skills & Requirements

next.jsreact nativeswiftkotlincomputer visionllmcloud
Response tip: Tailor to your strongest track, highlight real clinical data or model validation.
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