Over the past few years, tech companies saw a slowdown in hiring and growth. In 2023, departures outpaced new hires for the first time in this generation of startups.

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But times have changed. 

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This year, tech companies have stepped on the gas and accelerated their hiring efforts. According to Redpoint’s 2024 State of Talent Report, new job postings increased by 16% in Q1 2024 after a flat 2023. 

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As teams ramped up hiring, one area in particular started generating a lot of buzz: AI and ML engineering. Data science and machine learning saw the largest increase in job postings in 2023 and grew by almost 10% in Q1 2024, according to the Redpoint report. 

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Let’s take a deeper dive into what these roles are, the differences in AI and ML engineering salary compared to other software engineers, and how to approach this in your organization’s compensation strategy.

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Exploring AI and ML Engineering Roles

Not all AI and ML engineers are the same. There are a few ways to bucket these roles, but one simple way is to look at the 1% and the 99%.

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The 1% are the extreme outlier engineers or researchers building the future of AI. These people typically have PhDs, or in some cases have published research without a PhD that has moved the industry forward. Because of their unique skill set, folks in this 1% demand a very high premium when it comes to compensation.

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The vast majority, however, are standard ML engineers that most companies will be looking to hire. Standard ML engineers are software engineers adopting AI and ML best practices, taking the research done by the 1% and figuring out how to use it to solve business problems. While they’re not the extreme outliers, they’re still being compensated at premium prices, typically receiving salaries higher than software engineers.

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That said, with AI continuing to grow in popularity, learning how to use the technology will be a necessity for all software engineers. While there will probably still be non-ML software engineers in the future, this space is sure to evolve and change when it comes to both roles and compensation.

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The Rising Demand for AI and ML Engineering 

It’s not surprising that most tech companies are spending time and resources hiring AI and ML engineers. Incorporating AI into products and processes is necessary for organizations to keep pace with the market. McKinsey reports that 72% of businesses have adopted AI in at least one business function. 

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Data from Pave shows that compared with software engineers (SWE), hiring for ML engineers rose in step with each other from about 2020 to 2022. In late 2022, ML engineering took a huge leap when Chat GPT launched and took the world by storm. Since then, the hiring rates for these roles more than doubled SWE hiring rates.

‍Pave Founder & CEO Matt Schulman shared his take on the demand for AI engineers—see what the LinkedIn community had to say.

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Trends in AI and ML Engineering Salary & Equity

The AI hype has fueled dramatic trends in compensation for these roles. The top 1% of AI and ML engineers and researchers are being recruited by very well-known companies, like OpenAI and Anthropic. They’re often being compensated at extreme multiples of a typical engineer’s salary. There are stories of tech leaders offering ML engineers $1M+ compensation packages. Observer reported Elon Musk offers AI engineers salaries between $180,000 and $440,000. 

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Pave data shows that ML engineers in our dataset tend to earn higher salaries than SWEs. In public companies, ML engineers demand a ~20% salary premium. 

Equity offers companies another lever to pull for ML engineering compensation. Pave data shows the median annual equity for ML engineers trends higher than SWEs—and it’s a more dramatic bump for those at public companies.

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With a growing need for AI and ML talent, compensation leaders must be prepared to spend more than they would for traditional software engineers. Hiring and retaining AI talent is expensive but necessary for companies to stay competitive. 

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“If you’re not adopting AI into your product and operations, your company is probably going to go extinct. I view the same with labor. If you’re not adopting the latest AI trends, you will also go extinct,” said Matt Schulman, Founder & CEO of Pave.

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Should You Create Separate AI Job Families?

Since AI and ML engineering is a relatively new role, compensation leaders are currently building the plane as they fly it when it comes to job architecture. Whether it makes sense to create specific AI or ML job families will likely depend on a company’s size or stage.

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When we polled the audience of about 200 compensation leaders at Pave’s Total Rewards Live event, the group was split. About half said they have one core SWE job family, while the other half said they have a standalone ML job family.  

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For those in the startup world, it may not make sense to build out targeted job families for small teams. Keeping things simple can be best at this stage. One strategy is to have a job family for SWE, and plan to pay a 10% premium above that for the AI or ML engineering salary.

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For larger organizations, it might make sense to get more granular. By tapping into market data that’s specific to AI and ML, larger companies can build out a framework to offer more competitive compensation for these roles. That said, competing on compensation with the likes of OpenAI, Anthropic, and the Magnificent Seven will still be a challenge.

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Future-proof Your Compensation Strategy

Managing compensation and rewards requires staying on top of the trends, and right now, AI and ML engineering roles are on the rise. Compensation leaders must be prepared to offer lucrative packages to stay competitive, but figuring out salary figures for a newer role is easier said than done.  

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Pave partnered with Greenhouse to bring you Offer Insights, which gives you access to the top offer trends based on offer and acceptance data from over 7,500 companies. Start exploring the data so you can build a competitive compensation strategy for existing and emerging roles.

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Learn more about Offer Insights today.

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