About the company

Medical errors kill 6 million patients every year and are the third leading cause of death worldwide. Ferrum provides doctors with an automated quality management system and machine learning marketplace that ensures they catch and fix medical errors before they affect patient care. The service does not affect physician workflow, takes less than a day to install, and is delivered via a secure appliance so patient data never leaves the hospital. Ferrum has been deployed at hospitals in multiple countries around the world. We are a highly technical team led by experienced founders who have built, funded, and scaled successful healthcare technology companies previously.

Do they allow remote work?

Remote work is possible, see the description below for more information.

Salary: 80000 – 140000 USD

Job description

We’re looking for someone to join our Engineering organization at Ferrum and help grow our Enterprise Applications team, where we tackle the unique challenges of building a robust, scalable machine learning platform.

We are looking for a track record of:

* 4+ years of experience in a backend engineering role
* 2+ years of developing with statically typed languages, preferably Go
* 2+ years of experience developing for Linux systems
* Strong time management and organizational skills
* Excellent verbal and written communication skills
* Collaborating in and working with a remote, distributed team with an overlap between 8 AM and 12 PM Pacific

Preferred experience with, but not required:

* Exposure to DICOM, HL7, and healthcare information systems
* Significant experience working with SQL and PostgreSQL
* Deep understanding of Linux internals with a focus on storage and networking
* Working knowledge of distributed systems, concurrency management, and parallel systems

You will be delivering secure and well-tested solutions that scale. Examples of that include:

* Scale up the platform and integrations between machine learning algorithms and the pipeline
* Design and build the APIs interfacing the data pipeline and machine learning services
* Building security tools to protect sensitive patient data throughout the data pipeline
* Optimize bare-metal appliances to meet the high-performance needs of the application
* Provision infrastructure for the secure services coordinating on-premise and cloud-hosted services