Machine Learning Research Engineer, Health AI

Minimum qualifications: Bachelor's degree or equivalent practical experience. 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree. 2 years of experience in an applied research setting. Preferred qualifications: Knowledge in computer science, with experience in data structures, algorithms, and software design. About the job Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at scale, and extend well beyond web search. We are looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. At Google, our users come first, and the Systems Infrastructure team is at the heart of that promise. We build the technologies that transform the way we think about doing business. Whether working on our cloud systems, researching the latest in computer technology or keeping Google's internal systems humming, Googlers and users alike rely on us to keep things running. We're back-end experts: protecting your privacy and ensuring your security. Responsibilities Innovate, implement and evaluate machine learning algorithms over multimodal healthcare data. Translate the application of research into product use cases. Design, implement and deliver complex pieces of research and infrastructure as part of a multi-disciplinary team. Evaluate large and complex medical datasets. Work with a variety of internal and external stakeholders, including Product Managers, Clinicians, UX researchers and Regulatory Advisors.