HHS Policy Also Caps Indirect Salaries Paid Using Award Funds
The direct salary for individuals under NIH grant and cooperative agreement awards cannot exceed “Executive Level II” of the Federal Executive pay scale. Effective since January 1, 2024, the salary limitation for Executive Level II is $221,900.
In the November 14, 2024 Guide notice, NIH clarified that the same salary limitation applies to indirect salaries, e.g., executive salaries in various uncapped cost pools. For example, a business official whose salary is paid using indirect costs accumulated from multiple NIH awards cannot exceed the Executive Level II limit of $221,900.
The Office of Personnel Management—not NIH—is responsible for setting Executive Pay Scale levels.
Small Business Recipients—Get Help to Hone Your Product Pitch
If you didn’t already know, the NIH Portfolio Company Showcase is a targeted initiative that supports small business program recipients through one-on-one coaching followed by placement in presentation and attendance slots at industry events.
The next deadline for eligible companies to submit a request to participate is January 17, 2025, at 5 p.m. Eastern Time. Find additional details about the program at Showcase Opportunities for NIH Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Awardees and Partnering and Investment Opportunities.
Direct NIAID-specific questions to our small business team at NIAIDSBIR@mail.nih.gov.
Read Summary of Recent Artificial Intelligence Use Cases at NIH
Last month, Dr. Michael Lauer, NIH’s Deputy Director for Extramural Research, published the blog post Using Artificial Intelligence and Other Digital Technologies to Enhance Grant Management Operations to summarize how NIH is using artificial intelligence (AI) to better manage grant applications and awards.
As a reminder, our two routine AI-related warnings for grant applicants are as follows: 1) researchers who use AI are still responsible for following NIH’s rules around plagiarism, and 2) researchers must take care to protect private and confidential information, which may limit the materials used to train AI tools.