Middle ML (NLP) Engineer (US working hours) in Ralabs

We are seeking a Middle ML (NLP) Engineer who is passionate about revolutionizing the medical records industry.

Project Idea

Our client project aims to modernize the way medical records are handled and transferred. They are addressing the issue of outdated and inefficient practices in patient data management, which often involve fax machines and can lead to lost or delayed information, unnecessary reordering of tests, and even malpractice claims.

The solution is to digitize the transfer and release of patient data, providing a more reliable and secure method of handling this critical information. This digital approach is intended to improve patient outcomes by ensuring that all relevant health data is readily available when and where it’s needed.

Tech stack

Main tasks/use cases should be mainly categorization, summarization, and a lot of language tasks like that. We use Python and a few different models, OpenAI, Llama3.

The team

From the client’s side, there will be at least 10 very strong software engineers from the heart of software development and innovation — San Francisco.
On Ralabs side we have 2 Full stack developers and a Project Manager.

Working hours?

You will be able to communicate directly with the US team. It is important to be ready to work during the US hours (Approximately 21-22 CET).

On Ralabs side we have 2 Full stack developers and a Project Manager.

How many stages of the interview are there?

— Interview with the Recruiter — up to 30 min.;

— Technical interview — up to 1 h;

— Interview with the client in English — up to 1 h.

Requirements:

• 2+ years of working experience as an ML engineer;
• Working experience with Python and ML tools for Python;
• Working experience with OpenAI, Llama3;
• Working experience in AI/ML;
• Upper-intermediate level of English.

Responsibilities:

• Communication with the client about requirements;
• Direct communication with the development team and customers;
• Participation in sprint planning and task evaluation;
• Work with engineering best practices, e.g. conduct thorough code reviews to ensure code quality.