Salary of a pytorch programmer in Uruguay

Explore the average salary of a programmer according to seniority and Skills. Use the calculator for more accurate results based on your search.

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Calculate salary based on skills and experience

Salary based on Seniority

In Uruguay, junior developers with pytorch profile have an average salary of 2000 dollars per month. Senior profiles, with more experience, can reach salaries of up to 4800 dollars.

Salary in:

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Min
Half
Max
junior
$ 2000.00
$ 2500.00
$ 3000.00
mid
$ 2600.00
$ 3250.00
$ 3900.00
senior
$ 3200.00
$ 4000.00
$ 4800.00

*The last update of the data in this report is from 2026. Coming from internal sources, discover how Talently works. Here.

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Frequently asked questions about pytorch in Uruguay

Uruguay is the largest exporter of software per capita in Latin America and a global leader in software services. Its software development ecosystem is highly mature, driven by successful home-grown tech firms (like dLocal) and foreign multinationals establishing local development offices.

Uruguay has highly favorable tax laws that exempt income derived from exporting software and related services from corporate income tax (IRAE) under specific conditions. This strongly incentivizes building software for international markets.

Uruguay has a relatively high cost of living compared to the LATAM average. As a result, local tech employers must pay higher base salaries to sustain developers' purchasing power and stay competitive against international recruiters.

PyTorch is used in deep learning, computer vision, NLP, generative models, applied research, model training, and AI prototypes. It is common in machine learning engineer, data scientist, AI engineer, and applied researcher roles.

PyTorch continues to grow because of its adoption in research, AI products, and modern models. Demand increases when companies experiment with advanced machine learning, vision, language, and intelligent automation.

Python, machine learning, NumPy, Pandas, CUDA, MLOps, NLP, computer vision, Transformers, experiment tracking, and model deployment complement PyTorch well. Evaluation, data, and optimization knowledge are also valuable.

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