Report: Machine-learning engineer is the fourth fastest-growing job in the US, per LinkedIn

Two members of LinkedIn’s AI team told us why that’s the case.
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· 3 min read

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On Tuesday, LinkedIn released annual data on worldwide trends in hiring, employment, and workplaces.

Spoiler alert: Emerging-tech jobs are on the up-and-up—especially when it comes to AI.

Last year, the role of “artificial intelligence practitioners” came in last (no. 15) on the company’s list of jobs on the rise. But this year’s list, which expanded to 25 roles, ranks machine learning engineer as the fourth fastest-growing job in the US over the past five years.

  • Other tech roles that made the cut: user experience researcher, technical program manager, back end developer, and site reliability engineer.

We chatted with Romer Rosales, LinkedIn’s senior director of AI, and Souvik Ghosh, a principal staff engineer and director of AI, about why ML engineers are such a hot commodity.

On the rising demand for machine learning engineers

SG: “One of the trends we saw on this year’s broader list was an increase in jobs available to people with fewer years of experience. We’re seeing that machine learning engineer roles are only requiring four years of experience on average, which is great for someone who is just starting out.”

RR: “The consistent, high demand for machine learning engineers is not surprising, but the increased demand toward the end of last year and into this year was not something we could have easily predicted. This trend is interesting to see and may reflect the optimism in the industry, despite the pandemic and economic conditions. This data also tells us that the demand for AI practitioners has not yet peaked.”

On the future of AI jobs

SG: “I expect that ML, AI, and related roles will increase further and climb in the next few years as demand for talent skilled in ML continues to grow…Over time, I expect ML will become more broadly accessible. As this happens, I believe that a large number of software engineers will be able to take care of basic ML tasks while ML experts will focus on solving the most challenging problems.”

On the AI skills gap

SG: “The demand for AI-related roles is a reflection of the gap. As humans, we like to automate as much as we can…As this demand increases, we need more skilled talent to enable more automation. The barrier for companies to apply AI in their products and to solve their problems used to be high, but that barrier is getting lowered, and more and more companies now are eager to venture into exploring AI—which is also fueling demand.”

RR: “Given the limited talent in this space, I have seen a trend with more companies either training engineers to be proficient in this space, and/or making it easier for engineers who may not have built that proficiency yet to contribute to ML development.”

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