{ "url": "https://www.spotsalary.com/salary/machine-learning-engineer/colombia-co", "canonical": "https://www.spotsalary.com/salary/machine-learning-engineer/colombia-co", "headline": "Machine Learning Engineer Salary in Colombia", "description": "Median Machine Learning Engineer salary in Colombia is COP 14.8K per year. Compare P10–P90 salary bands by experience, take-home pay and a five-year outlook.", "occupation": { "name": "Machine Learning Engineer", "slug": "machine-learning-engineer", "family": "technology", "soc_code": "15-2051", "aliases": [ "ML Engineer", "AI Engineer" ], "description": null }, "location": { "name": "Colombia", "slug": "colombia-co", "type": "country", "iso2": "CO", "currency": "COP" }, "period": 2026, "confidence": 50, "status": "published", "method": "model", "updated_at": "2026-10-31T14:46:22+00:00", "salary_bands": [ { "level": "entry", "years_experience": [ 0, 2 ], "p10": 6202.83, "p25": 7719.08, "p50": 9648.84, "p75": 11192.66, "p90": 13398.11, "base_share": 95, "bonus_share": 5, "equity_share": 0 }, { "level": "mid", "years_experience": [ 3, 5 ], "p10": 9542.81, "p25": 11875.5, "p50": 14844.38, "p75": 17219.48, "p90": 20612.48, "base_share": 88, "bonus_share": 8, "equity_share": 4 }, { "level": "senior", "years_experience": [ 6, 9 ], "p10": 15745.64, "p25": 19594.58, "p50": 24493.22, "p75": 28412.13, "p90": 34010.58, "base_share": 80, "bonus_share": 10, "equity_share": 10 }, { "level": "executive", "years_experience": [ 10, 15 ], "p10": 26719.88, "p25": 33251.4, "p50": 41564.25, "p75": 48214.53, "p90": 57714.93, "base_share": 65, "bonus_share": 15, "equity_share": 20 } ], "take_home": { "currency": "COP", "tax_rate": 0, "top_rate": null, "method": "progressive", "label": "progressive brackets", "standard_deduction": 0, "rebate": 0, "source": "PwC Worldwide Tax Summaries", "source_url": "https://taxsummaries.pwc.com/colombia/individual/taxes-on-personal-income", "retrieved_at": "2026-10-23T00:00:00.000000Z", "verified": true, "notes": "general income basket; UVT=52,347 COP for FY2026", "bands": [ { "level": "entry", "gross": 9648.84, "taxable": 9648.84, "tax": 0, "effective_rate": 0, "net_annual": 9648.84, "net_monthly": 804.07, "method": "progressive", "top_rate": null, "standard_deduction": 0, "rebate": 0, "source": "PwC Worldwide Tax Summaries", "source_url": "https://taxsummaries.pwc.com/colombia/individual/taxes-on-personal-income", "retrieved_at": "2026-10-23T00:00:00.000000Z", "verified": true, "notes": "general income basket; UVT=52,347 COP for FY2026" }, { "level": "mid", "gross": 14844.38, "taxable": 14844.38, "tax": 0, "effective_rate": 0, "net_annual": 14844.38, "net_monthly": 1237.0316666666665, "method": "progressive", "top_rate": null, "standard_deduction": 0, "rebate": 0, "source": "PwC Worldwide Tax Summaries", "source_url": "https://taxsummaries.pwc.com/colombia/individual/taxes-on-personal-income", "retrieved_at": "2026-10-23T00:00:00.000000Z", "verified": true, "notes": "general income basket; UVT=52,347 COP for FY2026" }, { "level": "senior", "gross": 24493.22, "taxable": 24493.22, "tax": 0, "effective_rate": 0, "net_annual": 24493.22, "net_monthly": 2041.1016666666667, "method": "progressive", "top_rate": null, "standard_deduction": 0, "rebate": 0, "source": "PwC Worldwide Tax Summaries", "source_url": "https://taxsummaries.pwc.com/colombia/individual/taxes-on-personal-income", "retrieved_at": "2026-10-23T00:00:00.000000Z", "verified": true, "notes": "general income basket; UVT=52,347 COP for FY2026" }, { "level": "executive", "gross": 41564.25, "taxable": 41564.25, "tax": 0, "effective_rate": 0, "net_annual": 41564.25, "net_monthly": 3463.6875, "method": "progressive", "top_rate": null, "standard_deduction": 0, "rebate": 0, "source": "PwC Worldwide Tax Summaries", "source_url": "https://taxsummaries.pwc.com/colombia/individual/taxes-on-personal-income", "retrieved_at": "2026-10-23T00:00:00.000000Z", "verified": true, "notes": "general income basket; UVT=52,347 COP for FY2026" } ] }, "outlook": { "direction": "growing", "inflation_pct": 3, "real_growth_pct": 10, "forecasts": [ { "year": 2027, "scenario": "base", "p25": 15700.6, "p50": 16774.15, "p75": 17847.69, "confidence": 44, "drivers": [ { "name": "Inflation (CPI)", "note": "3.0% annual price growth", "weight": "high", "direction": "steady" }, { "name": "Wage growth", "note": "3.0% nominal wage trend", "weight": "high", "direction": "up" }, { "name": "Role demand trend", "note": "10.0% annual role demand", "weight": "high", "direction": "up" }, { "name": "Hiring demand", "note": "Demand index 50/100", "weight": "medium", "direction": "steady" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "10.0% after 3.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "13.0% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2027, "scenario": "conservative", "p25": 15492.19, "p50": 16551.48, "p75": 17610.78, "confidence": 44, "drivers": [ { "name": "Inflation (CPI)", "note": "3.0% annual price growth", "weight": "high", "direction": "steady" }, { "name": "Wage growth", "note": "3.0% nominal wage trend", "weight": "high", "direction": "up" }, { "name": "Role demand trend", "note": "10.0% annual role demand", "weight": "high", "direction": "up" }, { "name": "Hiring demand", "note": "Demand index 50/100", "weight": "medium", "direction": "steady" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "10.0% after 3.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "13.0% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2027, "scenario": "optimistic", "p25": 15909.02, "p50": 16996.82, "p75": 18084.61, "confidence": 44, "drivers": [ { "name": "Inflation (CPI)", "note": "3.0% annual price growth", "weight": "high", "direction": "steady" }, { "name": "Wage growth", "note": "3.0% nominal wage trend", "weight": "high", "direction": "up" }, { "name": "Role demand trend", "note": "10.0% annual role demand", "weight": "high", "direction": "up" }, { "name": "Hiring demand", "note": "Demand index 50/100", "weight": "medium", "direction": "steady" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "10.0% after 3.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "13.0% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2029, "scenario": "base", "p25": 19876.75, "p50": 21418.91, "p75": 22961.07, "confidence": 32, "drivers": [ { "name": "Inflation (CPI)", "note": "3.0% annual price growth", "weight": "high", "direction": "steady" }, { "name": "Wage growth", "note": "3.0% nominal wage trend", "weight": "high", "direction": "up" }, { "name": "Role demand trend", "note": "10.0% annual role demand", "weight": "high", "direction": "up" }, { "name": "Hiring demand", "note": "Demand index 50/100", "weight": "medium", "direction": "steady" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "10.0% after 3.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "13.0% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2029, "scenario": "conservative", "p25": 19095.66, "p50": 20577.22, "p75": 22058.78, "confidence": 32, "drivers": [ { "name": "Inflation (CPI)", "note": "3.0% annual price growth", "weight": "high", "direction": "steady" }, { "name": "Wage growth", "note": "3.0% nominal wage trend", "weight": "high", "direction": "up" }, { "name": "Role demand trend", "note": "10.0% annual role demand", "weight": "high", "direction": "up" }, { "name": "Hiring demand", "note": "Demand index 50/100", "weight": "medium", "direction": "steady" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "10.0% after 3.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "13.0% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2029, "scenario": "optimistic", "p25": 20678.86, "p50": 22283.25, "p75": 23887.64, "confidence": 32, "drivers": [ { "name": "Inflation (CPI)", "note": "3.0% annual price growth", "weight": "high", "direction": "steady" }, { "name": "Wage growth", "note": "3.0% nominal wage trend", "weight": "high", "direction": "up" }, { "name": "Role demand trend", "note": "10.0% annual role demand", "weight": "high", "direction": "up" }, { "name": "Hiring demand", "note": "Demand index 50/100", "weight": "medium", "direction": "steady" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "10.0% after 3.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "13.0% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2031, "scenario": "base", "p25": 25161.82, "p50": 27349.81, "p75": 29537.79, "confidence": 30, "drivers": [ { "name": "Inflation (CPI)", "note": "3.0% annual price growth", "weight": "high", "direction": "steady" }, { "name": "Wage growth", "note": "3.0% nominal wage trend", "weight": "high", "direction": "up" }, { "name": "Role demand trend", "note": "10.0% annual role demand", "weight": "high", "direction": "up" }, { "name": "Hiring demand", "note": "Demand index 50/100", "weight": "medium", "direction": "steady" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "10.0% after 3.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "13.0% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2031, "scenario": "conservative", "p25": 23535.54, "p50": 25582.11, "p75": 27628.68, "confidence": 30, "drivers": [ { "name": "Inflation (CPI)", "note": "3.0% annual price growth", "weight": "high", "direction": "steady" }, { "name": "Wage growth", "note": "3.0% nominal wage trend", "weight": "high", "direction": "up" }, { "name": "Role demand trend", "note": "10.0% annual role demand", "weight": "high", "direction": "up" }, { "name": "Hiring demand", "note": "Demand index 50/100", "weight": "medium", "direction": "steady" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "10.0% after 3.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "13.0% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2031, "scenario": "optimistic", "p25": 26876.79, "p50": 29213.9, "p75": 31551.01, "confidence": 30, "drivers": [ { "name": "Inflation (CPI)", "note": "3.0% annual price growth", "weight": "high", "direction": "steady" }, { "name": "Wage growth", "note": "3.0% nominal wage trend", "weight": "high", "direction": "up" }, { "name": "Role demand trend", "note": "10.0% annual role demand", "weight": "high", "direction": "up" }, { "name": "Hiring demand", "note": "Demand index 50/100", "weight": "medium", "direction": "steady" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "10.0% after 3.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "13.0% modelled nominal growth", "weight": "high", "direction": "up" } ] } ] }, "skills": [], "sources": [ { "name": "SpotSalary deterministic wage model", "url": null, "type": "deterministic_model", "tier": "derived" } ], "faqs": [ { "question": "What is the average salary of a Machine Learning Engineer in Colombia?", "answer": "SpotSalary models a median annual salary of COP 14.8K for a Machine Learning Engineer in Colombia, or about COP 1,237 per month before tax. The middle 50% of the estimate ranges from COP 11.9K to COP 17.2K, with a full P10–P90 range of COP 9.5K to COP 20.6K. This is a modelled estimate, not a job offer." }, { "question": "How much does an entry-level Machine Learning Engineer earn in Colombia?", "answer": "The entry-level band for a Machine Learning Engineer in Colombia centres on COP 9.6K per year (P25–P75: COP 7.7K–COP 11.2K). Entry-level pay typically covers the first 2 years of experience and depends on company size, skills and responsibilities." }, { "question": "How much does a senior or executive Machine Learning Engineer earn in Colombia?", "answer": "The executive band for a Machine Learning Engineer in Colombia has a median of COP 41.6K per year and a P75 of COP 48.2K. Senior and executive packages often include a larger bonus and equity component; this benchmark shows the total-compensation split for each level." }, { "question": "What is the salary outlook for a Machine Learning Engineer in Colombia?", "answer": "The outlook is growing. In the base scenario, median pay reaches COP 27.3K by 2031, about 84.2% above today before inflation and 10.0% per year in real terms after 3.0% inflation. Scenario bands widen over time and are not guarantees." }, { "question": "What is the take-home pay for a Machine Learning Engineer in Colombia?", "answer": "After an effective income tax rate of 0.0%, the mid-level Machine Learning Engineer in Colombia takes home about COP 14,844 per year, or COP 1,237 per month. The estimate uses imported national tax brackets and does not include social contributions or local levies." }, { "question": "How does SpotSalary calculate these salary estimates?", "answer": "Estimates are anchored to external market signals — GDP per capita PPP, inflation, wage growth, cost of living, demand and automation exposure — and synthesised with AI. Deterministic validation rejects implausible outputs. This report carries 50% confidence." } ], "related": { "occupation_locations": [ { "headline": "Machine Learning Engineer Salary in Kuala Lumpur, Malaysia", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/kuala-lumpur-my-1458988644" }, { "headline": "Machine Learning Engineer Salary in Pristina, Kosovo", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/pristina-xk-1901760068" }, { "headline": "Machine Learning Engineer Salary in Belgium", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/belgium-be" }, { "headline": "Machine Learning Engineer Salary in Mexico", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/mexico-mx" }, { "headline": "Machine Learning Engineer Salary in Iceland", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/iceland-is" }, { "headline": "Machine Learning Engineer Salary in Iran", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/iran-ir" }, { "headline": "Machine Learning Engineer Salary in Cuba", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/cuba-cu" }, { "headline": "Machine Learning Engineer Salary in Guinea", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/guinea-gn" }, { "headline": "Machine Learning Engineer Salary in Indonesia", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/indonesia-id" }, { "headline": "Machine Learning Engineer Salary in Australia", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/australia-au" }, { "headline": "Machine Learning Engineer Salary in New Zealand", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/new-zealand-nz" }, { "headline": "Machine Learning Engineer Salary in Malaysia", "url": "https://www.spotsalary.com/salary/machine-learning-engineer/malaysia-my" } ], "occupation_hub": "https://www.spotsalary.com/occupations/machine-learning-engineer", "location_hub": "https://www.spotsalary.com/locations/colombia-co", "all_salaries": "https://www.spotsalary.com/salaries" }, "citation": { "plain": "Machine Learning Engineer Salary in Colombia. SpotSalary. https://www.spotsalary.com/salary/machine-learning-engineer/colombia-co", "updated_at": "2026-10-31T14:46:22+00:00" } }