{ "url": "https://www.spotsalary.com/salary/machine-learning-engineer/bangladesh-bd", "canonical": "https://www.spotsalary.com/salary/machine-learning-engineer/bangladesh-bd", "headline": "Machine Learning Engineer Salary in Bangladesh", "description": "Median Machine Learning Engineer salary in Bangladesh is ৳9.7M 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": "Bangladesh", "slug": "bangladesh-bd", "type": "country", "iso2": "BD", "currency": "BDT" }, "period": 2026, "confidence": 50, "status": "published", "method": "nearest_neighbor", "updated_at": "2026-11-02T06:01:54+00:00", "salary_bands": [ { "level": "entry", "years_experience": [ 0, 2 ], "p10": 3104992.58, "p25": 3770348.27, "p50": 4435703.96, "p75": 5101059.64, "p90": 5988200.43, "base_share": 95, "bonus_share": 5, "equity_share": 0 }, { "level": "mid", "years_experience": [ 3, 5 ], "p10": 4949625.18, "p25": 6993696.01, "p50": 9731612.85, "p75": 12098627.38, "p90": 13566757.07, "base_share": 88, "bonus_share": 8, "equity_share": 4 }, { "level": "senior", "years_experience": [ 6, 9 ], "p10": 11240012.1, "p25": 13648586.28, "p50": 16057161.2, "p75": 18465735.37, "p90": 21677166.87, "base_share": 80, "bonus_share": 10, "equity_share": 10 }, { "level": "executive", "years_experience": [ 10, 15 ], "p10": 13375353.36, "p25": 16241500.56, "p50": 19107647.39, "p75": 21973794.59, "p90": 25795324.07, "base_share": 65, "bonus_share": 15, "equity_share": 20 } ], "take_home": { "currency": "BDT", "tax_rate": null, "top_rate": 30, "method": "top_rate_only", "label": "top rate only", "standard_deduction": 0, "rebate": 0, "source": "PwC Worldwide Tax Summaries", "source_url": "https://taxsummaries.pwc.com/bangladesh?topicTypeId=35fd5f5e-24ba-48d0-a39a-b76315a497dc", "retrieved_at": "2026-10-22T00:00:00.000000Z", "verified": true, "notes": "30", "bands": [ { "level": "entry", "gross": 4435703.96, "taxable": null, "tax": null, "effective_rate": null, "net_annual": null, "net_monthly": null, "method": "top_rate_only", "top_rate": 30, "standard_deduction": 0, "rebate": 0, "source": "PwC Worldwide Tax Summaries", "source_url": "https://taxsummaries.pwc.com/bangladesh?topicTypeId=35fd5f5e-24ba-48d0-a39a-b76315a497dc", "retrieved_at": "2026-10-22T00:00:00.000000Z", "verified": true, "notes": "30" }, { "level": "mid", "gross": 9731612.85, "taxable": null, "tax": null, "effective_rate": null, "net_annual": null, "net_monthly": null, "method": "top_rate_only", "top_rate": 30, "standard_deduction": 0, "rebate": 0, "source": "PwC Worldwide Tax Summaries", "source_url": "https://taxsummaries.pwc.com/bangladesh?topicTypeId=35fd5f5e-24ba-48d0-a39a-b76315a497dc", "retrieved_at": "2026-10-22T00:00:00.000000Z", "verified": true, "notes": "30" }, { "level": "senior", "gross": 16057161.2, "taxable": null, "tax": null, "effective_rate": null, "net_annual": null, "net_monthly": null, "method": "top_rate_only", "top_rate": 30, "standard_deduction": 0, "rebate": 0, "source": "PwC Worldwide Tax Summaries", "source_url": "https://taxsummaries.pwc.com/bangladesh?topicTypeId=35fd5f5e-24ba-48d0-a39a-b76315a497dc", "retrieved_at": "2026-10-22T00:00:00.000000Z", "verified": true, "notes": "30" }, { "level": "executive", "gross": 19107647.39, "taxable": null, "tax": null, "effective_rate": null, "net_annual": null, "net_monthly": null, "method": "top_rate_only", "top_rate": 30, "standard_deduction": 0, "rebate": 0, "source": "PwC Worldwide Tax Summaries", "source_url": "https://taxsummaries.pwc.com/bangladesh?topicTypeId=35fd5f5e-24ba-48d0-a39a-b76315a497dc", "retrieved_at": "2026-10-22T00:00:00.000000Z", "verified": true, "notes": "30" } ] }, "outlook": { "direction": "growing", "inflation_pct": 6, "real_growth_pct": 13.5, "forecasts": [ { "year": 2027, "scenario": "base", "p25": 10885003.6, "p50": 11629277.36, "p75": 12373551.11, "confidence": 44, "drivers": [ { "name": "Inflation (CPI)", "note": "6.0% annual price growth", "weight": "high", "direction": "up" }, { "name": "Wage growth", "note": "7.0% 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 60/100", "weight": "medium", "direction": "up" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "13.5% after 6.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "19.5% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2027, "scenario": "conservative", "p25": 10748371.76, "p50": 11483303.16, "p75": 12218234.57, "confidence": 44, "drivers": [ { "name": "Inflation (CPI)", "note": "6.0% annual price growth", "weight": "high", "direction": "up" }, { "name": "Wage growth", "note": "7.0% 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 60/100", "weight": "medium", "direction": "up" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "13.5% after 6.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "19.5% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2027, "scenario": "optimistic", "p25": 11021635.45, "p50": 11775251.55, "p75": 12528867.65, "confidence": 44, "drivers": [ { "name": "Inflation (CPI)", "note": "6.0% annual price growth", "weight": "high", "direction": "up" }, { "name": "Wage growth", "note": "7.0% 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 60/100", "weight": "medium", "direction": "up" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "13.5% after 6.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "19.5% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2029, "scenario": "base", "p25": 15411202.08, "p50": 16606898.8, "p75": 17802595.51, "confidence": 32, "drivers": [ { "name": "Inflation (CPI)", "note": "6.0% annual price growth", "weight": "high", "direction": "up" }, { "name": "Wage growth", "note": "7.0% 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 60/100", "weight": "medium", "direction": "up" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "13.5% after 6.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "19.5% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2029, "scenario": "conservative", "p25": 14838118.03, "p50": 15989351.32, "p75": 17140584.62, "confidence": 32, "drivers": [ { "name": "Inflation (CPI)", "note": "6.0% annual price growth", "weight": "high", "direction": "up" }, { "name": "Wage growth", "note": "7.0% 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 60/100", "weight": "medium", "direction": "up" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "13.5% after 6.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "19.5% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2029, "scenario": "optimistic", "p25": 15998855.3, "p50": 17240145.79, "p75": 18481436.29, "confidence": 32, "drivers": [ { "name": "Inflation (CPI)", "note": "6.0% annual price growth", "weight": "high", "direction": "up" }, { "name": "Wage growth", "note": "7.0% 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 60/100", "weight": "medium", "direction": "up" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "13.5% after 6.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "19.5% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2031, "scenario": "base", "p25": 21817861.32, "p50": 23715066.65, "p75": 25612271.99, "confidence": 30, "drivers": [ { "name": "Inflation (CPI)", "note": "6.0% annual price growth", "weight": "high", "direction": "up" }, { "name": "Wage growth", "note": "7.0% 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 60/100", "weight": "medium", "direction": "up" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "13.5% after 6.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "19.5% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2031, "scenario": "conservative", "p25": 20482486.96, "p50": 22263572.78, "p75": 24044658.61, "confidence": 30, "drivers": [ { "name": "Inflation (CPI)", "note": "6.0% annual price growth", "weight": "high", "direction": "up" }, { "name": "Wage growth", "note": "7.0% 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 60/100", "weight": "medium", "direction": "up" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "13.5% after 6.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "19.5% modelled nominal growth", "weight": "high", "direction": "up" } ] }, { "year": 2031, "scenario": "optimistic", "p25": 23221993.66, "p50": 25241297.45, "p75": 27260601.25, "confidence": 30, "drivers": [ { "name": "Inflation (CPI)", "note": "6.0% annual price growth", "weight": "high", "direction": "up" }, { "name": "Wage growth", "note": "7.0% 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 60/100", "weight": "medium", "direction": "up" }, { "name": "Automation exposure", "note": "20% exposure", "weight": "high", "direction": "steady" }, { "name": "Real wage growth", "note": "13.5% after 6.0% inflation", "weight": "high", "direction": "up" }, { "name": "Scenario growth", "note": "19.5% modelled nominal growth", "weight": "high", "direction": "up" } ] } ] }, "skills": [], "sources": [ { "name": "Iran", "url": null, "type": "nearest_donor", "tier": "derived" } ], "faqs": [ { "question": "What is the average salary of a Machine Learning Engineer in Bangladesh?", "answer": "SpotSalary models a median annual salary of ৳9.7M for a Machine Learning Engineer in Bangladesh, or about ৳810,968 per month before tax. The middle 50% of the estimate ranges from ৳7M to ৳12.1M, with a full P10–P90 range of ৳4.9M to ৳13.6M. This is a modelled estimate, not a job offer." }, { "question": "How much does an entry-level Machine Learning Engineer earn in Bangladesh?", "answer": "The entry-level band for a Machine Learning Engineer in Bangladesh centres on ৳4.4M per year (P25–P75: ৳3.8M–৳5.1M). 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 Bangladesh?", "answer": "The executive band for a Machine Learning Engineer in Bangladesh has a median of ৳19.1M per year and a P75 of ৳22M. 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 Bangladesh?", "answer": "The outlook is growing. In the base scenario, median pay reaches ৳23.7M by 2031, about 143.7% above today before inflation and 13.5% per year in real terms after 6.0% inflation. Scenario bands widen over time and are not guarantees." }, { "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 and draws on 6 salary observations." } ], "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/bangladesh-bd", "all_salaries": "https://www.spotsalary.com/salaries" }, "citation": { "plain": "Machine Learning Engineer Salary in Bangladesh. SpotSalary. https://www.spotsalary.com/salary/machine-learning-engineer/bangladesh-bd", "updated_at": "2026-11-02T06:01:54+00:00" } }