Pave

NEW: The 2025 AI & ML Compensation Trends & Practices Report is here! Our data team mined Pave’s real-time dataset of 8,500+ customers to find the most valuable insights about these hot jobs to bring compensation leaders a clearer picture of the market. With thought leadership from our partners at Nua Group LLC, this is a can’t-miss resource for any compensation professional with AI/ML roles in their organization. Download your copy today: https://lnkd.in/gTqszUsN #Pave #Compensation #AI #ML

Compensation for software engineers varies by company stage... but how much exactly? Explore the latest findings from Matt and the Pave data team:

At Pave, our data team fields tons of questions about org charts. Things like: How big should my R&D team be? How many HRBPs should I have on staff? How many direct reports should our CEO have? To answer these common questions, we dug into our dataset and shared the findings on our blog. Ready to dive into some of the most popular org chart benchmarks and see how your company stacks up? Explore this helpful roundup: https://lnkd.in/guamr8Kb #Pave #Compensation

This Employee Appreciation Day, we have to give a big shoutout to all Pavers, the best team in the game! Pave is made up of an all-star group of smart, talented people who play to win and put the customer first. We’re really feeling that hug of jawn today (iykyk). If you want to be a part of the magic happening here, check out our open roles: https://lnkd.in/gmJza_Nv ?? #Pave #Compensation

?? Did you know? Pave customers save roughly 17 days between their first and second merit cycles with Pave's Compensation Planner. So whether you're finishing a cycle, or gearing up for the next one, we're here to help make merit cycles FASTER. ? Our team will be at Transform in Vegas at Booth #339, and we're ready to chat all things compensation. Grab some time with us: https://lnkd.in/gJkWK7jC

Transform is right around the corner! Don’t miss the chance to connect with the Pave team at booth #339. See our latest features in action, chat with our compensation experts, and maybe even win big! ?? Whether you’re refining pay structures or tackling new challenges, we’d love to meet you. ?? See you in Vegas! ?? ? #Pave #Compensation #HRTransform

Compa ratios of tenured employees vs. new hires: which R&D candidates have the most offer letter negotiation leverage? A couple weeks ago, we looked at the preferential treatment that candidates tend to get over employees as demonstrated by the compa differentials between candidates & employees. In short, we found that “New Hires” somewhat consistently have higher compa ratios than “Tenured Employees”, especially at the more senior IC levels. This is likely due in part to the fact that employees have the most negotiation leverage when they are candidates at the point of the offer letter. This “compa ratio disparity” between candidates and employees can also serve as a new assessment of market demand if you break down the results by job family. Let’s take a look. _______________ The Results: Our data science team took a look at compa ratio distributions for companies in Pave’s dataset where we have access to reliable salary ranges. Results were filtered to only include P3 employees. Then, the results were broken down into two mutually exclusive groups: [A] “New Hires” – employees who joined within the last 12 months [B] “Tenured Employees” – all employees who have more than 12 months of tenure Lastly, the results were broken down by job family. And to keep things interesting for this post, we decided to only look at the prominent R&D job families. _______________ Takeaways: 1?? ????/???? ?????????????????????? ???????????? ?????????????????? ???? ???? ??????-????????. The median compa ratio for AI/ML engineers is 1.05 for “New Hires” and 0.97 for “Tenured Employees”. This “compa ratio disparity” is more pronounced than the other R&D job families analyzed and serves as yet another signal of the heightened market demand for AI/ML talent. 2?? ?????????????????? ???? ?????? ?????? ???????????????? ???????? ???????????????? ?????????????????????? ???????????????? ???????? ???????? ?????? ???????????????????? ???? ?????? ?????????? ???? ?????? ?????????? ????????????. Candidate leverage is somewhat present across the board but is most pronounced in the “hot” job families. 3?? ???????? ?????????? ?????????? ?????????? ???????????? ?????? ?????????????? ???? ???????? ?????? ????????????????????????? Offer letter practices vary. I have heard customers make strong cases for two approaches: [??] “??????-????-??????” (e.g. 0.85 to 1.0 compa ratio) => allows recruiters to anchor towards a lower range so that candidates do not feel mild demoralized for being placed so low in the range. [??] “??????-????-??????” (e.g. 0.85 to 1.15 compa ratio) => more transparent for candidates, highlighting more upward raise potential within the job level for employees who perform well over time. Which of the two approaches do you think is optimal? #pave #compa #benchmarks

A strong ownership culture is the foundation of an engaged, high-performance organization. But as any compensation or HR leader will tell you, fostering ownership takes more than just giving employees equity and calling it a day. In our latest guest post, Robyn Shutak, CEP, FGE, NDEF of Infinite Equity shares 8 tips for building a thriving culture of ownership and accountability. Dive into her expert advice on the blog ?? https://lnkd.in/gHK4MyXW

Big news for the Total Rewards community: Today, we’re excited to announce a new strategic partnership between Pave and?Newfront! This collaboration combines Newfront's advanced insurance brokerage solutions with Pave's compensation expertise to help organizations design competitive, equitable, and comprehensive rewards packages for their teams. Read more about the partnership below ??

Compa ratios of tenured employees vs. new hires: do candidates get preferential treatment? When I was a software engineer, I used to joke with my coworkers that the easiest way to get a raise would be to resign, re-interview with the same company, and then re-negotiate a new cash & equity package (not to mention a fresh sign-on bonus). Is this fact or a fallacy? Do candidates get preferential treatment over loyal, tenured employees? One way to analyze this is under the lens of compa ratios. Let’s take a look. __________________ The Results: Our data science team took a look at compa ratio distributions for companies in Pave’s dataset where we have access to reliable salary ranges. And they broke down the results into two mutually exclusive groups: [A] “New Hires” – employees who joined within the last 12 months? [B] “Tenured Employees” – all employees who have more than 12 months of tenure In theory, employees from group B have been at the company (and also in their current levels) longer and thus had more time to progress to higher compa ratios in their salary range. But in practice, this theory breaks down. According to Pave’s analysis of 66k real-time incumbent datapoints, it is Group A–the “New Hires”–that somewhat consistently has the higher compa ratio across nearly all IC levels. __________________ Takeaways: 1?? ?????????????????? ???????? ?????? ???????? ?????????????????????? ???????????????? ???????? ???????? ?????? ????????????????????, ???? ?????? ?????????? ???? ?????? ?????????? ????????????. Some may view this as inconsistent or unfair with existing, tenured employees. Others may view compa-ratio-flexing as the elbow grease required to win great talent. 2?? ?????????? ?????????? ?????????????????? ?????????????? ?????? ?????? ?????????????? ?????????????????? ???? ?? ?????? ???????????????????? ???? ???????????? ????????????. If you break down the “compa ratio disparity” benchmarks between new hires and tenured employees across the dimensions of job family and/or location, it can offer a new metric to help assess the hottest or coldest jobs. For instance, Pave’s Data Science team analyzed the AI/ML Engineering job family and found that the median compa ratio is 1.05 for “New Hires” and 0.97 for “Tenured Employees”. This is yet another signal of the rising market demand for AI/ML talent. 3?? ?????????? ???????????? ?????????????????? ????????. I meet some companies who have a philosophy of bringing in candidates from “min to mid” (i.e. 0.85 to 1.0) compa ratio. Other companies believe that in practice, it should be “whatever it takes” (i.e. min to max). The findings of this post suggest that there is more of a bias towards the latter than the former, but we can take a more detailed look at the breakdown between these philosophies in a future post. #pave #compa #benchmarks