Machine Learning Engineer

A biotechnology company in the digital pathology and virtual staining space is developing AI-powered technology that replaces traditional chemical staining with virtual staining solutions. The company’s platform generates high-quality digital stains in minutes while preserving tissue, helping laboratories get more insights from every sample.

The company develops advanced solutions for virtual staining and digital pathology workflows, enabling multiple stain outputs from a single tissue sample and supporting the transition from research applications toward clinical deployment. Its technology helps improve lab efficiency, reduce chemical waste, and expand diagnostic possibilities.

This is a fully remote role for a Senior Machine Learning Engineer who will lead core work in representation learning and generative modeling, developing advanced computer vision algorithms for digital pathology and virtual staining products.

Responsibilities

• Design, train, fine-tune, and optimize deep learning models for digital pathology and virtual staining applications.
• Develop computer vision models for large-scale medical image and pathology data.
• Build Python-based ML pipelines for data preprocessing, model training, evaluation, and inference.
• Work with advanced architectures such as Vision Transformers, Diffusion Models, and GANs.
• Optimize model performance, scalability, and inference efficiency in GPU-based environments.
• Collaborate with engineering, product, and domain experts to integrate ML models into core product features.

Required Qualifications

• B.Sc. in Computer Science, Software Engineering, Physics, Electrical Engineering, or a related field from a recognized academic institution – must.
• At least 2 years of experience as a Machine Learning Engineer – must.
• Hands-on experience working with image and/or video data – must.
• Strong Python development experience – must.
• Experience with medical imaging and/or digital pathology.
• Experience with advanced Transformer architectures and Hugging Face.
• Experience with GPU optimization tools and frameworks such as CUDA, TensorRT, or ONNX.
• Experience with MLOps tools such as MLflow or Kubeflow.
• Familiarity with FDA regulation in the medical AI field – advantage.

CV to: [email protected]