Investment Banker vs Machine Learning Engineer Salary
Investment Bankers earn approximately 17.5% more than Machine Learning Engineers nationally — $200,000 vs $165,000.
Investment Bankers earn more today, but the more useful difference is direction: Machine Learning Engineer employment is growing much faster than average while Investment Banker is growing about as fast as average.
What it takes to get in
Investment Banker roles typically require a bachelor's degree, while Machine Learning Engineer roles require a master's degree. Adding up the whole path to a first real job, including any apprenticeship, residency, or exam, comes to about 4 years for Investment Banker and 6 for Machine Learning Engineer.
Only one of the two is gated by licensing: Investment Banker requires Series 79 and 63 (FINRA) required to work on deals, while Machine Learning Engineer does not. That cuts both ways — a license is a real barrier when you are trying to enter, and a real moat once you hold one, because it limits how quickly the labor supply can grow.
Pay range, not just the median
The medians hide most of what matters. Investment Banker pay runs from roughly $75,000 at the low end to $400,000 at the high end; Machine Learning Engineer runs $95,000 to $250,000. Where you land inside that band depends far more on specialization, employer, and metro than on the job title.
The two bands are shaped differently. Investment Banker top earners make about 5.3× what beginners make, against 2.6× for Machine Learning Engineer. A wide band means the ceiling rewards specializing and the median understates what is achievable; a narrow one means pay is more predictable and less dependent on landing the right niche.
Where each field is heading
The Bureau of Labor Statistics projects Investment Banker employment to grow 3% over the decade to 2034 — growing about as fast as average — against growth of 20% for Machine Learning Engineer.
What the work is actually like
Investment Banker: Corporate office, client and deal-team meetings; very long hours, frequent travel for live deals. Machine Learning Engineer: Office or remote, building and deploying ML models into production systems.
Then there is when the work happens — irregular hours that vary week to week for Investment Banker against standard weekday hours for Machine Learning Engineer. Over years this shapes sleep, childcare, and whether you see anyone on weekends.
Location flexibility is not symmetric here. Machine Learning Engineer work can be done fully remotely, while Investment Banker work is rarely remote. That changes which metros you can earn a high salary from, and therefore what a given salary is actually worth after housing.
Moving between the two
Crossing between Finance and Tech means rebuilding credentials and network from close to zero — the day-to-day skills do not carry, and neither does the résumé. Highly competitive lateral entry from Big 4 transaction advisory or equity research; base+bonus comp makes seniorPay wildly variable and understates top-decile bonus years. Software engineers with self-taught ML skills and data scientists with stronger engineering chops both funnel into this role; pure researchers usually need to build production/deployment experience to switch in.
If you are entering rather than switching: Investment Banker — Bachelor's (often finance/econ from a target school) into a 2-year analyst program at a bank, or MBA into associate track; Series 79/63 licensing required on the job. Machine Learning Engineer — No dedicated BLS occupation exists; it's tracked under Software Developers. Typical path is a CS/ML master's or strong self-directed ML portfolio, often after 1-2 years as a software engineer or data scientist.
Pay ranges, education requirements, and employment projections come from the Bureau of Labor Statistics Occupational Outlook Handbook and Occupational Employment and Wage Statistics. See our methodology for how these are compiled and updated.
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