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Neko Health is building preventative healthcare through non-invasive body scanning and data-driven clinical insights. The company combines proprietary imaging technology with direct clinical care to deliver personalized, actionable health insights before symptoms appear.
As a Data Science Lead in Computer Vision & ML, you will own the development and deployment of computer vision and machine learning algorithms that power Neko's clinical decision support systems. You'll work across imaging challenges including skin analysis, tissue imaging, and body measurements, collaborating closely with hardware engineers, software engineers, clinical scientists, and other data scientists.
In your first 6–12 months, you will:
- Develop, verify, validate, and deploy computer vision and ML algorithms for clinical use, contributing to product features and research breakthroughs that improve member outcomes
- Own problems end-to-end: from initial questions and messy real-world data through modeling, evaluation, and production deployment
- Partner with hardware engineers, software engineers, medical doctors, and clinical researchers to bridge the gap between sensor capture and clinical application
- Combine classical, geometric, and deep learning methods, using semi-supervised, self-supervised, or weakly-supervised approaches where expert labels are limited
- Deliver production-quality Python code integrated into Neko's backend infrastructure, leveraging AI tools while maintaining ownership and judgment
- Mentor colleagues, review code and designs, and foster a culture of openness and continuous improvement
You bring extensive experience in modern computer vision and deep learning on real-world camera or sensor data (detection, segmentation, tracking, 3D perception). You have a proven track record shipping algorithms to production under challenging real-world conditions (lighting, motion, optics, calibration). You possess strong ML fundamentals, algorithmic thinking, and breadth across problem types. Your Python skills are production-grade, with solid testing and code review practices. You communicate clearly across disciplines—explaining trade-offs to clinicians, engineers, and managers alike. You're AI-fluent and motivated to apply rigorous science to preventative healthcare.
REQUIREMENTS:
- 5+ years of relevant industry experience, or 2+ years post-PhD
- Extensive experience in modern computer vision and deep learning on real-world camera or sensor data (detection, segmentation, tracking, 3D perception), with judgment to know when classical or geometric methods are better
- Track record of making algorithms work under messy, real-world capture conditions and shipping to production
- Strong machine learning fundamentals and algorithmic thinking, with breadth beyond one niche
- Strong software engineering skills in Python, production-level code, testing, and code review
- Experience working in cross-functional R&D teams alongside hardware and software engineers
- Clear communication across disciplines
- AI fluent—confident using latest tooling to increase productivity and efficiency
- MSc or PhD in Computer Vision, Machine Learning, Computer Science, Physics, Engineering, or related field
- Motivated to apply strong science to improve preventative healthcare
PREFERRED:
- Experience with multi-view geometry, matching across views and time (tracking, registration, calibration, SLAM, re-identification)
- Experience with 3D vision or reconstruction
- Experience with label-efficient learning (semi-supervised, self-supervised, pseudo-labelling) and large-scale data pipelines
- Experience with sensor fusion, state estimation, filtering, or non-RGB imaging (thermal)
- Experience teaching or mentoring engineers or researchers in high-growth, mission-driven organizations
- Exposure to safety-critical or regulated settings
