Machine Learning Engineer vs NLP Engineer: Which Pays More?
Side-by-side salary comparison by city, experience level, and career growth outlook. Data reflects current market rates.
Machine Learning Engineer
Design and deploy machine learning models to production systems.
NLP Engineer
Build natural language processing systems and text AI models.
Machine Learning Engineer earns more on average — the national median is $0/year (0%) higher than a NLP Engineer. However, salaries vary significantly by city, employer, and experience level — see the city-by-city breakdown below.
Machine Learning Engineer vs NLP Engineer — Salary by City
National median figures in USD across top cities.
| City | Machine Learning Engineer | NLP Engineer | Difference |
|---|---|---|---|
| San Francisco, CA | $173,682 | $175,055 | Machine $1,373 |
| New York, NY | $135,980 | $135,980 | Machine +$0 |
| Seattle, WA | $174,792 | $174,364 | Machine +$428 |
| Austin, TX | $134,974 | $137,212 | Machine $2,238 |
| Chicago, IL | $129,773 | $129,954 | Machine $181 |
| Boston, MA | $157,928 | $156,119 | Machine +$1,809 |
| London, UK | £78,216 | £94,915 | Machine $16,699 |
| Toronto, Canada | CA$134,265 | CA$156,823 | Machine $22,558 |
Frequently Asked Questions
Does a Machine Learning Engineer or NLP Engineer earn more?+
A Machine Learning Engineer earns more on average. The national median salary for a Machine Learning Engineer is $120,230/year, compared to $120,230/year for a NLP Engineer — a difference of $0 (0%).
Which has better career growth — Machine Learning Engineer or NLP Engineer?+
Machine Learning Engineer roles are growing at 45% YoY while NLP Engineer demand is growing at 42% YoY. Machine Learning Engineer has stronger near-term demand growth.
Can you switch from Machine Learning Engineer to NLP Engineer?+
Yes. Many professionals transition between these roles, especially since both are in the same category. Shared skills include: Python, PyTorch.
Which is harder to automate — Machine Learning Engineer or NLP Engineer?+
NLP Engineer has a lower AI automation risk (20% vs 22%). Based on Oxford Martin School and McKinsey 2023 analysis.