Machine Learning Engineer

We’ve validated the idea with veterinary professionals, and now we're ready to build a working MVP model and API that can analyze skin photos and return diagnostic predictions.

????? What You’ll Build
You’ll own the core machine learning component for the MVP:

Train a multi-class image classification model on labeled skin photos

Implement transfer learning using ResNet, EfficientNet, or similar

Output predictions with confidence scores (top-1 or top-3)

Add explainability (e.g. Grad-CAM heatmaps)

Wrap the model in a simple REST API (Flask or FastAPI)

Optionally: assist with lightweight deployment

???? Data
We'll start with a small dataset (~500–1,000 labeled images)

You’ll help design preprocessing and augmentation steps

Bonus if you’ve worked with medical or dermatology datasets before

???? Ideal Skills
Strong experience with image classification models (CNNs)

Python + PyTorch, TensorFlow, or FastAI

REST API development (Flask or FastAPI)

Experience with model explainability (Grad-CAM or similar)

Comfortable working with a small team (PM + vet advisor)

Bonus: experience with veterinary, medical, or dermatology imaging

? Deliverables
Trained model (.pt or .h5)

Jupyter notebook or Python script for training pipeline

REST API endpoint that accepts images and returns predictions

Grad-CAM heatmap generator (if possible)

Basic deployment instructions (local or cloud)

???? What We Offer
Fully remote, async-friendly work

Fast-moving MVP environment — work directly with the founder

Option to join longer-term if we continue beyond MVP

The chance to make a real impact in the veterinary industry