ScaleiQ · Digital Health / Machine Learning
About the engagement
ScaleiQ is developing a hybrid quantum approach to early disease detection, correlating biomarkers with optical coherence tomography (OCT) scans to surface faint, high-dimensional patterns that precede the clinical diagnosis of conditions like cancer and Alzheimer's. We're seeking an AI Engineer on a contract basis with deep computer vision experience — specifically with OCT imaging and high-dimensional, longitudinal data — to help build and train the models at the core of this work.
This is a contract/consulting role with the potential to grow as the company moves toward its seed round.
Scope of work
Design, train, and optimize convolutional neural networks (CNNs) and related architectures for medical image analysis, with a focus on OCT scans.
Work with high-dimensional, longitudinal imaging data — handling temporal sequences, patient-level tracking, and change detection over time.
Build reusable libraries, pipelines, and tooling for data preprocessing, model training, and evaluation at scale.
Develop training strategies capable of handling very large OCT datasets (potentially millions of images), including data augmentation, distributed training, and efficient handling of class imbalance and label scarcity.
Collaborate with our scientific and quantum teams to integrate classical CV pipelines with hybrid quantum methods, applying each where it performs best.
Contribute to reproducible benchmarking and rigorous evaluation against classical baselines.
What we're looking for
Strong background in deep learning for computer vision, with hands-on experience building and training CNNs.
Direct experience with medical imaging — OCT specifically is strongly preferred; related modalities (retinal imaging, MRI, CT, histopathology) are valued.
Experience working with high-dimensional and/or longitudinal datasets.
Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, or similar).
Track record of building libraries, reusable components, or production-grade training pipelines.
Experience training models on large-scale image datasets and a clear point of view on how you'd approach training on millions of OCT images.
Bonus: familiarity with self-supervised learning, foundation models for imaging, topological methods, or quantum machine learning.
Potential to transition into a longer-term or equity-based role as the company grows