We are building a privacy-first MLOps platform for data-driven organizations in healthcare and life sciences. The platform is designed to support the entire lifecycle of machine learning (ML) efforts to accelerate breakthrough medical research and bring clinical-grade ML solutions to market. Our quick-expanding strategic network includes early clinical and technology partners and organizations in the US, Israel and Europe.

Responsibilities

Job Description

Help conceptualize and build an MLOps platform around data management and federated learning; from initial design to full implementation and deployment
Work with the team to design and implement tools and APIs for a centralized system with distributed agents/workers
Build supplementary software components that enables data scientists to interact with the platform
Support integration with existing ML/DL/FL libraries
Develop highly scalable machine learning (computer vision)



models to solve problems such as medical image classification and segmentation
Develop in-house machine learning tools and pipelines to support fast experimentation of machine learning models
Work with other engineers to identify and solve machine learning problem

Requirements

Experience in one or more of the following areas: deep learning, computer vision,
Experience with machine learning frameworks such as TensorFlow, PyTorch or YOLO
Curiosity and minimal experience in Federated Learning & Self- Supervised Learning algorithms & applications
Expert knowledge in Python (object oriented design)
Expertise in API design with FastAPI
Through understanding of deploying ML models via Docker and Kubernetes at scale on-prem and cloud.

📌 Machine Learning (Jammu)
🏢 Gesund.ai
📍 Jammu