Machine learning and AI engineering
Currently the highest-paying individual contributor track in technology, driven by demand that has expanded far faster than the supply of people who can genuinely do the work.
The roles split into several types. Research scientists develop new methods and usually hold doctorates. Machine learning engineers build and deploy systems at scale — the largest category by headcount. Applied specialists integrate existing models into products, which is the most accessible entry point for engineers already working in software.
The premium is real but the bar is high. Strong mathematics, solid software engineering and practical experience with production systems are all expected, and interviews test all three.
Security
Security roles command a persistent premium because the shortage has lasted for years and the consequences of getting it wrong are severe and visible.
Security architects design defensible systems. Penetration testers and offensive security specialists attack systems to find weaknesses before others do. Incident responders handle breaches under time pressure. Application security specialists sit between engineering and security, which is currently one of the scarcest combinations.
Certifications carry more weight here than elsewhere in technology, and demonstrable practical work — capture-the-flag results, published research, bug bounty findings — is taken seriously by employers.
Infrastructure, cloud and platform engineering
The people who keep large systems running reliably are paid well, in part because the failure mode is highly visible.
Platform engineers, site reliability engineers, cloud architects and DevOps specialists all sit above the general software engineering band. Distributed systems expertise, deep knowledge of a major cloud provider, and experience operating systems at genuine scale are what drive the premium.
This is a strong route for engineers who prefer systems thinking to product work, and it transfers unusually well between industries — every large company runs infrastructure.
Data engineering and analytics
Data engineering has quietly become one of the better-paid specialisations, because every machine learning and analytics initiative depends on data infrastructure that mostly does not exist yet.
Data engineers build the pipelines and platforms; analytics engineers model data for analysis; data scientists extract insight and build models. Of the three, data engineering currently commands the strongest and most consistent premium, largely because it is less glamorous and therefore less crowded.
The skills are concrete and learnable: SQL to a genuinely high standard, a programming language, distributed processing frameworks, and cloud data platforms.
Product and engineering management
The two main leadership tracks pay comparably to senior individual contributor roles, and better at the top.
Product managers decide what gets built and why, which requires commercial judgement alongside technical fluency. Technical product managers in infrastructure and platform areas earn a premium over consumer-facing equivalents.
Engineering management progresses from team lead through director to vice president of engineering and chief technology officer. It is worth being clear that management is a different job rather than a promotion from engineering — the skills barely overlap, and many strong engineers are happier and equally well paid on the senior individual contributor track.
How technology compensation is structured
Understanding the components matters, because comparing base salaries across companies is misleading.
Base salary is guaranteed. Bonus is typically a percentage tied to company and individual performance. Equity — restricted stock at public companies, options at private ones — often represents the largest component at larger employers and vests over three or four years.
At a public company, equity is close to cash with a delay. At a startup, options may be worth a great deal or nothing at all, and understanding the strike price, the vesting schedule and the current valuation is essential before treating them as compensation. Always compare total packages rather than base figures.
Getting in
Technology remains more open to non-traditional backgrounds than most well-paid fields, though less so than during the peak hiring years.
What consistently works is demonstrable ability. A portfolio of real projects, open source contributions, a track record of shipping things. Employers can verify these, which is why they outweigh claims on a resume.
For those already in technology, the fastest route to higher pay is specialising into a scarce area rather than accumulating general experience. And moving companies still reliably produces larger increases than internal raises — internal progression is bounded by bands, external offers are priced at market.