How job-hopping is disrupting pay equity

What do you do when your best engineers start asking for 100% raises?

People running to money bag emojis

Tech salaries are increasing so quickly that some companies are struggling to keep pace.

Illustration: Protocol

It’s a troublesome trend for startup founders. An engineer spends two or three years at a company before getting an outside job offer that would double their salary. It leaves their current employer with a dilemma: Beat the offer — pushing that engineer’s salary far ahead of their colleagues’ — or lose more talent.

Tech salaries are increasing faster than many companies can keep up with, and that market is threatening startups’ efforts to pay employees in high-demand job areas — engineering, product, design and operations — within consistent salary bands. Ensuring pay equity is already a challenge for high-growth startups, and these pressures are making it even harder.

“We all want pay equity, but at the same time, there’s this tension, this market demand that is so real and is happening every day to our founders,” said Katie Hughes, executive talent head of the venture capital firm General Catalyst. “How do they respond in a way that allows them to maintain the most competitive workforce?”

Hughes calls it “re-trading” because, since the requests are so big, they’re more like renegotiating an offer than asking for a typical raise or equity refresh. Hughes has seen this happen again and again among GC’s portfolio companies.

“To have one of your top engineers come back and re-trade two years in because they have a super competitive offer — it’s really hard to manage, and I think we’re seeing a decent amount of that because the market has changed so quickly,” Hughes said.

How we got here

The successes of public companies over the last two years are partially to blame here: Tech companies that boomed on the pandemic-era stock market have had a ton of leverage to recruit candidates with both cash and stock, said Ashish Raina, managing consultant at Optimize Talent.

For pandemic darlings like Zoom and Peloton, “your ability to throw money at problems was a lot easier,” Raina said. Some public companies have since flopped or cut valuations, but this influx of generous compensation packages shot up the whole market average, including at startups.

Thus, many tech companies are now budgeting for bigger annual raises than usual. The 3% annual pay increases that Raina is used to seeing are now averaging 5% to 7% per year.

“You’re seeing a much more aggressive push in terms of saying, ‘Hey, we need to set aside more budget in order to retain talent and keep pace with the market,’” Raina said.

Two companies in Raina’s portfolio even decided to offer an across-the-board inflation increase, which Raina doesn’t typically recommend. One offered a 4% bump, and the other gave out a 7.5% increase.

But that’s likely not enough to bring about pay equity on its own as employees job-hop or re-trade their offers.

How to manage pay equity

The big problem here: As the market encourages job-hopping with big pay increases, employees with long tenures may fall behind frequent job-hoppers in terms of pay.

Companies have a retention interest in correcting for pay equity across categories like role, level and location. Credit Karma overhauled its entire compensation system, which cost $15 million and resulted in raises for 98% of employees. Startups are generally spending fewer resources in auditing for pay equity specifically across demographics like gender, race and ethnicity, Raina said.

“It’s not that companies don’t care about pay equity, but they often are facing structural issues and resourcing issues that prevent them from fully tackling those issues as well as they could,” Raina said. “Resourcing-wise, people aren’t spending as much time as they could on those topics.”

Hughes is advising her portfolio companies to be proactive about compensation: Look at the market and offer raises at least once or twice a year in order to keep up.

Once a year is really a minimum now. Many companies are taking a “reactive” or “as-needed” approach to catching up with the market, Raina said. Big companies have the resources to be more proactive, but that’s not the norm among the kinds of 200- to 800-employee startups that Raina works with.

Setting a higher baseline for minimum pay for each job category is also key. Companies may also want to refine their systems around performance assessment, Raina said: Every budget has a limit, so leaders need to prioritize and allocate.

“You may not be able to do something for everybody,” Raina said. “You have to figure out, ‘OK, what is it that I can actually afford to do?’”

And, Raina pointed out, ensuring total pay equity isn’t the goal in every situation.

“Not everyone is deserving of that, either, right? You might have someone who’s a low performer. You might have someone who took a ton of equity and got very low cash,” Raina said. “You realize this picture isn’t as clear-cut as it seems when you look at the actual individual employee.”


Judge Zia Faruqui is trying to teach you crypto, one ‘SNL’ reference at a time

His decisions on major cryptocurrency cases have quoted "The Big Lebowski," "SNL," and "Dr. Strangelove." That’s because he wants you — yes, you — to read them.

The ways Zia Faruqui (right) has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster.

Photo: Carolyn Van Houten/The Washington Post via Getty Images

“Cryptocurrency and related software analytics tools are ‘The wave of the future, Dude. One hundred percent electronic.’”

That’s not a quote from "The Big Lebowski" — at least, not directly. It’s a quote from a Washington, D.C., district court memorandum opinion on the role cryptocurrency analytics tools can play in government investigations. The author is Magistrate Judge Zia Faruqui.

Keep ReadingShow less
Veronica Irwin

Veronica Irwin (@vronirwin) is a San Francisco-based reporter at Protocol covering fintech. Previously she was at the San Francisco Examiner, covering tech from a hyper-local angle. Before that, her byline was featured in SF Weekly, The Nation, Techworker, Ms. Magazine and The Frisc.

The financial technology transformation is driving competition, creating consumer choice, and shaping the future of finance. Hear from seven fintech leaders who are reshaping the future of finance, and join the inaugural Financial Technology Association Fintech Summit to learn more.

Keep ReadingShow less
The Financial Technology Association (FTA) represents industry leaders shaping the future of finance. We champion the power of technology-centered financial services and advocate for the modernization of financial regulation to support inclusion and responsible innovation.

AWS CEO: The cloud isn’t just about technology

As AWS preps for its annual re:Invent conference, Adam Selipsky talks product strategy, support for hybrid environments, and the value of the cloud in uncertain economic times.

Photo: Noah Berger/Getty Images for Amazon Web Services

AWS is gearing up for re:Invent, its annual cloud computing conference where announcements this year are expected to focus on its end-to-end data strategy and delivering new industry-specific services.

It will be the second re:Invent with CEO Adam Selipsky as leader of the industry’s largest cloud provider after his return last year to AWS from data visualization company Tableau Software.

Keep ReadingShow less
Donna Goodison

Donna Goodison (@dgoodison) is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. She previously covered the public cloud at CRN after 15 years as a business reporter for the Boston Herald. Based in Massachusetts, she also has worked as a Boston Globe freelancer, business reporter at the Boston Business Journal and real estate reporter at Banker & Tradesman after toiling at weekly newspapers.

Image: Protocol

We launched Protocol in February 2020 to cover the evolving power center of tech. It is with deep sadness that just under three years later, we are winding down the publication.

As of today, we will not publish any more stories. All of our newsletters, apart from our flagship, Source Code, will no longer be sent. Source Code will be published and sent for the next few weeks, but it will also close down in December.

Keep ReadingShow less
Bennett Richardson

Bennett Richardson ( @bennettrich) is the president of Protocol. Prior to joining Protocol in 2019, Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company. Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB. Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University.


Why large enterprises struggle to find suitable platforms for MLops

As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

Photo: artpartner-images via Getty Images

On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. But this spring when the company was in the market for a machine learning operations platform to manage its expanding model roster, it wasn’t easy to find a suitable off-the-shelf system that could handle such a large number of models in deployment while also meeting other criteria.

Some MLops platforms are not well-suited for maintaining even more than 10 machine learning models when it comes to keeping track of data, navigating their user interfaces, or reporting capabilities, Matthew Nokleby, machine learning manager for Lily AI’s product intelligence team, told Protocol earlier this year. “The duct tape starts to show,” he said.

Keep ReadingShow less
Kate Kaye

Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories. She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of and is the author of "Campaign '08: A Turning Point for Digital Media," a book about how the 2008 presidential campaigns used digital media and data.

Latest Stories