I'm continuing the work on my SPAR project, and I met with a hiring manager at an AI Safety org this week. Here's some interesting things I've learned:

- They use a multiple of tools and processes  - a mixture of HR tools, AI and human review to score and review applications and work tests. They stated the process felt "disjointed".
- Work tests are developed for multiple roles are broken down into 2-3 types of tests:

- General - a foundational test that each applicant receives at this stage to map to cultural fit
- Role specific - work problem (based upon junior or senior role type) that is reviewed by human reviewers. 

- Work tests are developed by an internal team (not just the hiring manager) and they use archives of past (and high scoring responses) + team to create tests. Scoring (initial screening is done by AI with human review).

- Their biggest frictions in the hiring process: 

- Not being able to review in a timely manner completed work tests, and therefore their concern is they are losing high quality candidates to other orgs.
- When the potential tidal increase of more candidates coming, due to expected IPO's, then their own  hiring strategy may be to only hire from referrals. 

This conversation makes me realize that the hiring process is not only being hindered by processes (disjointed or non-existent) but also that most orgs (even with processes or tools) are not ready for the influx of people that could potentially look to AI Safety orgs as how they can contribute. 

Therefore, my two may crux's that I need to explore more (with more discussions with AI Safety hiring managers) are the following:

- Is the hiring process inefficient due to the process (or lack of it) and a Playbook for hiring managers would help?

- How are work tests being written, how are the being scored and is the "reviewer" knowledgeable about the specific role that they can determine if the applicant has not only done this work in the past (and knows what they are talking about) but also is it good work (and not just a basic understanding)? 

My ask for help: I'm looking for AI Safety founders or hiring managers who would be willing to conduct a short interview (15 mins) with me to help me understand the issues that you are facing. All information that you share with me will be only shared with my SPAR mentor, and my final published report will only include generialiations (and not specifics to any individual or org) in regards to your internal processes. I can be reached at: [email protected]   

For my SPAR project of "Hiring resources for new AI Safety orgs", this past week I've been scheduling meetings with hiring managers, and have spoken to some fantastic recruiters on the issues they are experiencing in AI Safety hiring. Here's a couple of interesting things I've learned this week:

- Screening call questions are not consistant, and feedback from canditates is that it's more a test about their alignment (and understanding) of AI Safety vs. using a common set of questions every time that screen someone that asks instead about their career goals, what they are good at, what they are not interested in doing, and what their previous peers/managers/co-workers would rate their work performance. 

- There also seems to a disconnect on how hiring managers are determining work tests for non-researcher or technical roles. This is an uncertainity that I want to explore more, as I'm unsure who is creating these work tests and who is grading them?

- A theme that keeps on coming up is the need for a "shared database" of canditates between AI Safety organizations. I heard this today from a recruiter, as a best practice from an outside field, and have a central theme on this forum for the need. 

- Most AI Safety orgs are ramping up hiring right now and include "HR" type roles (generally called generalist) that mention that they want to scale 2x in headcount this year. I had an interesting conversation with a recruiter that there is a difference in company culture, skill set, etc. for a canditate who can scale a business or team of 1-15 people vs. scaling from 20+people. When you're a smaller org, everyone is "all hands on deck", but when your organization grows, that tactic becomes unmanagable and unaligned with both what the individual contributors are focusing on, and what your organization needs. It's the understanding that it's two very different types of leaders who run those orgs. 

- I continue to build out my contact list of hiring managers and companies in AI Safety. I'm focusing on newer organizaitions - potentlally less than 10 people, and I'm looking for help with recommendations on who to speak to. Please reach out if you can help, thanks!