JOB DESCRIPTION SUMMARY

Want to make a difference in the healthcare domain and ship groundbreaking technology of large-scale systems with deep learning and artificial intelligence? We are looking for a highly motivated individual, passionate about building useful software solutions and keen on working with AI to build data analytics frameworks and tools to join GE Healthcare. As the Machine Learning Engineer, you will work with Data Scientists and Platform Engineers on building world-class software tools in the healthcare domain.
GE Healthcare is a leading global medical technology and digital solutions innovator. Our Digital organization is structured around three major areas: Products & Platform, Go to Market, and Centers of Excellence.

REQUIREMENTS:

You will be expected to design, develop, and maintain software frameworks, tools, and services. You need to have:

• Experience in Amazon AWS, microservices, and serverless technologies.
• Experience with inference deployment frameworks like ONNX, OpenVINO, TensorRT, etc.
• Practice strong software development/testing principles and best practices: Test-driven development (TDD), CI/CD, code refactoring, coding standards, etc.
• Evaluate new AI & software tools through POCs and propose solutions for implementation.
• Document guides, systems design, reference architectures, and implementations.
• Master’s Degree in a “STEM” major (Science, Technology, Engineering, Mathematics).
• 5+ years of professional experience.
• 2+ years of experience in public cloud infrastructure (AWS/other).
• Strong implementation experience with a variety of high-level languages and frameworks used in AWS server less environments.
• Knowledge in Machine Learning and Deep Learning is a plus.
• Excellent in Python, with knowledge of the features of the language, and best practices, coding conventions, relevant libraries.
• Experience in data visualization with 3rd party libraries (e.g., matplotlib, seaborn).
• Experience with VCS systems, specifically git.
• Experience with Linux (commands, bash scripting).
• Experience in writing documented & tested code and algorithms.

ELIGIBILITY REQUIREMENTS:

• Legal authorization to work in Hungary is required.
• Must be willing to work out of an office located in Budapest or Szeged.

ESSENTIAL RESPONSIBILITIES:

The Senior Machine Learning Engineer will be part of a cross-disciplinary team, consisting of software engineers, data scientists, data engineers, and medical professionals working on commercially facing development projects, typically involving large, complex data sets.
You will be mainly responsible for developing an analytics and intelligence software suite which enables early disease prediction by providing capabilities such as data collection, storage and sharing, traditional and AI analytics integration, hosting and execution, feature and prediction storage, hosting, sharing, and visualization.
In this role, you will have the opportunity to work in a truly agile team. You will work in a cross-functional team with many people of different expertise. Expect deep involvement in the project as well as clear and transparent communication from your leaders. You will have the opportunity to be proactive, bring your own ideas, and even challenge the status quo. You are expected to closely collaborate with your team as well as be able to execute tasks on your own up to completion. Your goals will be defined upfront, and your leaders will always be available to answer your questions.
You will work on a hybrid edge–cloud solution that is light on-premises and heavy on cloud, implemented in Amazon AWS. You will have the opportunity to participate in high-level design discussions to influence detailed software design, implement the solution, and guide others.

ROLES AND RESPONSIBILITIES

• Support the integration of AI models for IHI grants (Predictom, Synthia, and Thera4Care) and the national grant IMAGENE.
• Develop and maintain the AI Research Suite, a platform providing data connectivity, storage, AI analytics execution, and visualization of data and results.
• Collaborate with a cross-disciplinary team of data scientists, data engineers, software engineers, and medical professionals on commercially facing development projects involving large, complex data sets.
• Design, develop, and maintain data analytics frameworks, tools, and services.
• Evaluate new software tools through POCs and propose solutions for implementation.
• Document guides, systems design, reference architectures, and implementations.