Full-Time

IT Specialist

Confirmed live in the last 24 hours

Rad AI

Rad AI

51-200 employees

AI-driven software for radiology workflows

AI & Machine Learning
Healthcare

Mid

United States

Must be located in the Eastern Time Zone; Central Time Zone applicants considered if committed to EST hours.

Category
IT Support
System Administration
IT & Security
Requirements
  • 2+ years of experience in IT support, help desk, or a similar role.
  • Must be located in the Eastern Time Zone within the United States. We are open to considering applicants in the Central Time Zone if they are committed to EST hours.
  • Degree in Information Technology, Computer Science, or related field, or equivalent practical experience.
  • Proficiency in troubleshooting hardware and software issues on both macOS and Windows.
  • Experience with SaaS platforms and tools such as Google Workspace, Okta, and Slack, or similar.
  • Understanding of networking fundamentals, including laptop connectivity, VPN, printing, etc.
  • Ability to participate in an on-call schedule that may include after-hours and weekend support.
  • Relevant IT certifications (e.g., CompTIA A+, Network+, Microsoft 365 Certified).
  • Experience in a fast-growing startup environment.
  • Demonstrated ability to identify and implement IT process improvements.
  • Experience creating technical documentation or user guides.
Responsibilities
  • Prepare and manage IT tasks related to onboarding and offboarding employees.
  • Configure and deploy laptops and peripherals for new employees.
  • Provide technical support to new hires during their first week.
  • Track and maintain inventory of IT equipment, including replacements and returns.
  • Create and update knowledge base articles, guides, and IT process documentation.
  • Assist with day-to-day operations as needed to maintain smooth operations.
  • Helpdesk Support: Tier I - Basic Support: Address account access issues, password resets, hardware troubleshooting, and basic connectivity issues. Provide support for VC systems, including Zoom and Google Meet. Manage ticket triage, documentation, and resolution. Escalate complex issues to appropriate teams when necessary. Tier II - Advanced Support: Troubleshoot software and hardware issues and resolve intermediate network problems like VPN and WiFi connectivity. Administer SaaS tools like Google Workspace and Okta. Diagnose and resolve root cause issues impacting IT systems or tools.

Rad AI enhances radiology workflows using artificial intelligence to improve efficiency and accuracy in medical imaging. Its main product, Omni Reporting, automates routine tasks and ensures follow-up on incidental findings, making reporting more accurate for radiologists. Unlike competitors, Rad AI focuses specifically on integrating AI into existing radiology practices, providing a subscription-based model that allows for continuous software updates. The company's goal is to streamline healthcare processes while maintaining high standards of data security and patient privacy, as evidenced by its SOC 2 Type II and HIPAA certifications.

Company Stage

Series B

Total Funding

$76.8M

Headquarters

San Francisco, California

Founded

2018

Growth & Insights
Headcount

6 month growth

13%

1 year growth

43%

2 year growth

82%
Simplify Jobs

Simplify's Take

What believers are saying

  • Rad AI achieved a 48% increase in radiograph reporting efficiency at RANT.
  • Rad AI raised $50M in Series B funding, boosting its expansion capabilities.
  • Strategic collaboration with AGFA HealthCare enhances Rad AI's market position.

What critics are saying

  • Emerging competition from companies like DeepMind could overshadow Rad AI's offerings.
  • Rapid AI technology evolution requires Rad AI to continuously innovate.
  • AI-driven automation may face resistance from the medical community.

What makes Rad AI unique

  • Rad AI's Omni Reporting won 'Best New Radiology Software' by AuntMinnie.
  • Rad AI integrates AI with FHIRcast for enhanced radiology workflow interoperability.
  • Rad AI is a pioneer in using large language models for radiology report generation.

Help us improve and share your feedback! Did you find this helpful?